Monte Carlo Simulations of Light-Skin Interactions: Implications for Therapeutic and Oncological Applications

Monte Carlo Simulations of Light-Skin Interactions: Implications for Therapeutic and Oncological Applications | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 3 August 2025 V1 Latest version Share on Monte Carlo Simulations of Light-Skin Interactions: Implications for Therapeutic and Oncological Applications Authors : Mahdi Qaryan 0000-0002-7641-204X [email protected] , Iman Kafian-Attari , and Isaac O. Afara Authors Info & Affiliations https://doi.org/10.22541/au.175422254.45857165/v1 Published Photodiagnosis and Photodynamic Therapy Version of record Peer review timeline 450 views 207 downloads Contents Abstract Mahdi Qaryan, Iman Kafian-Attari, and Isaac O. Afara Introduction Methods Fundamentals of Light Propagation in Skin Tissue Absorption dynamics in skin tissue Scattering phenomena and tissue heterogeneity Reflection and refraction at the tissue interfaces Key optical properties defining light-skin interaction Skin multilayer architecture and optical variability Wavelength-dependent behavior of light in skin Penetration depth and targeted therapeutic interventions Thermal effects and selective photothermolysis Photobiomodulation and cellular response activation Modeling light distribution in multilayered skin with MC simulations Monte Carlo simulations in clinical applications Results Discussion Acknowledgments References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background and Objective: Monte Carlo (MC) simulations are essential tools for understanding light–skin interactions, with applications in dermatology and oncology. This review provides foundational insights into light propagation in skin tissue, focusing on absorption, scattering, and optical properties relevant to therapeutic and diagnostic contexts. Methods: Relevant literature was selected based on the use of MC simulations in light–skin modeling, emphasizing therapeutic dermatology and cancer research. Studies were categorized by spectral range, penetration depth, and simulation approach to identify trends. Results: MC methods were examined across applications such as photodynamic therapy, photothermal therapy, and radiotherapy. The review also discusses how MC simulations capture constituent-based optical variations, vascular alterations, and structural changes in cancerous tissues. Trends in spectral range, depth of penetration, and simulation frameworks highlight the adaptability of MC models. Conclusions: MC simulations offer a powerful framework for advancing skin-related cancer diagnostics and treatments, while also guiding future research directions. Mahdi Qaryan, Iman Kafian-Attari, and Isaac O. Afara 1 School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane QLD 4072, Australia 2 Department of Technical Physics, University of Eastern Finland, Kuopio, Finland E-mail: [email protected] Abstract. Background and Objective: Monte Carlo (MC) simulations are essential tools for understanding light–skin interactions, with applications in dermatology and oncology. This review provides foundational insights into light propagation in skin tissue, focusing on absorption, scattering, and optical properties relevant to therapeutic and diagnostic contexts. Methods: Relevant literature was selected based on the use of MC simulations in light–skin modeling, emphasizing therapeutic dermatology and cancer research. Studies were categorized by spectral range, penetration depth, and simulation approach to identify trends. Results: MC methods were examined across applications such as photodynamic therapy, photothermal therapy, and radiotherapy. The review also discusses how MC simulations capture constituent-based optical variations, vascular alterations, and structural changes in cancerous tissues. Trends in spectral range, depth of penetration, and simulation frameworks highlight the adaptability of MC models. Conclusions: MC simulations offer a powerful framework for advancing skin-related cancer diagnostics and treatments, while also guiding future research directions. Introduction Understanding the interaction of light in skin is crucial in the context of diagnostic and therapeutic assessment applications related to skin pathologies and cancer. Therapies such as laser- and radiation-based treatments have become integral techniques in dermatology and oncology, requiring a deep understanding of their mechanisms and effects [1, 2]. Sunlight, a major source of light, is a key factor in the development of skin cancer, particularly through the induction of DNA lesions [3]. The role of sunlight in the development of skin cancer has been recognized for over a century, with pigmentation playing a protective role [4–6]. UV radiation, a component of sunlight, is a significant environmental risk factor for skin cancer, causing direct DNA damage and indirect oxidative damage [7]. The specific interaction between UV radiation and melanocytes, the cells responsible for skin pigmentation, is a key area of research in understanding the development of cutaneous melanoma [8, 9]. The importance of modeling light-tissue interaction in dermatological applications is underscored by its impact on skin appearance and health [10, 11]. This modeling is particularly relevant in the context of skin spectral properties and light scattering, which can be simulated using computer models [11]. The roughness of skin interfaces and index mismatch also play a role in light propagation [12]. Understanding these interactions is crucial for the development of photomedicine domain, as it enables the noninvasive detection and early intervention for skin conditions [13]. Like other biological tissues, the optical properties of skin, including absorption and scattering, are key factors that influence how light interacts in the tissue [14, 15]. Hyperspectral modeling approach has been proposed as a way to predict skin appearance attributes [16], and the use of laser-skin interaction modeling software can help correct for variations in melanin concentration on different skin types [17]. Monte Carlo (MC) simulations have significantly advanced our understanding of light-skin interaction in therapeutic and oncological contexts. These simulations have been used to study the influence of skin phototypes on light propagation and penetration depth [18], and to optimize light delivery in photodynamic therapy [19]. They have also been applied in the study of skin cancer using polarized Monte Carlo simulations [20, 21], and to investigate the effect of melanin on the tissue in pulse oximetry [22]. Furthermore, Monte Carlo simulations have been used to measure light dosimetry in biological tissue [23], and to simulate light- skin interaction in medical hyperspectral imaging applications [24]. These studies collectively demonstrate the significance of Monte Carlo simulations in enhancing our understanding of light-skin interaction in various medical contexts. Light-based therapies and skin cancer research have evolved significantly, with the develop- ment of MC simulation approaches playing a crucial role in their advancement. Hence, the importance of MC simulation for modeling light interaction in skin cannot be overempha- sized, both from the research and application perspectives [1, 25]. These simulations have enabled a deeper understanding of the light-skin interaction, leading to the development of more effective and targeted therapies. Recent advancements in MC simulations have focused on improving the accuracy and speed of these simulations, particularly in the field of particle therapy [25–27]. In the context of light-based skin therapies, a comprehensive understanding of light-skin interaction is essential for development of new treatment protocols and improve- ment of existing ones [1]. The use of near-infrared (NIR) light in light-mediated theranostics has emerged as a promising approach, with the potential to address the limitations of conven- tional strategies [28]. Furthermore, several recent patents have been granted for light-based therapies such as photodynamic therapy [19], photothermal therapy, photoimmunotherapy as well as applying nanotechnology [29–32], which offer potential for targeted skin tissue destruction with minimal impact on healthy parts [33]. Radiation-based therapies also rely heavily on accurate modeling of dose distribution within tissues, a task well-suited to Monte Carlo simulation due to its stochastic nature and high spatial precision [34]. These advance- ments underscore the critical role of MC simulations in the ongoing evolution of light- and radiation-based therapies. Objectives and Scope of the Review This review aims to provide a comprehensive guide for researchers and clinicians on the applications of Monte Carlo simulations in understanding light-skin interactions, with a specific focus on therapeutic and oncological contexts. By exploring the fundamental principles, optical properties, and propagation mechanisms of light within skin tissue, this review highlights the critical role of Monte Carlo simulations in optimizing treatment modalities such as photodynamic therapy, radiotherapy, and photothermal therapy. Additionally, it examines the utility of these simulations in cancer research, particularly in identifying optical, vascular, and morphological changes associated with malignancies. Our scope extends to a detailed analysis of the current state of Monte Carlo simulation techniques, addressing their limitations and potential advancements. This review serves as a valuable resource for those aiming to leverage these simulations for precise diagnostic and therapeutic applications, offering insights into future trends and research areas. By bridging the gap between theoretical modeling and clinical practice, we aim to enhance the efficacy and accuracy of light-based treatments for skin conditions and cancers. Methods The articles included in this review were carefully selected from a comprehensive initial list of hundreds of studies. These studies span the domain of Monte Carlo simulations for light-skin interaction, specifically focusing on skin-related applications in therapeutic dermatology and cancer research. The selection process specially for the Results section of the paper, utilized prominent databases, including PubMed, Web of Science, Scopus, ScienceDirect, Optica, and PLOS One, ensuring broad coverage of relevant literature. To construct the core section of the review, a targeted search strategy was employed using key phrases such as “Monte Carlo simulation”, “skin”, ”optics”, “cancer”, “photodynamic therapy”, “photothermal therapy”, and “radiotherapy.” These words and phrases were intentionally searched within the full text of articles, rather than being limited to titles or abstracts, to capture even those papers that are partially aligned with the scope of this review. This approach minimized the risk of overlooking valuable insights and ensured a comprehensive inclusion of studies. The methodology adopted for this review reflects a hybrid approach, incorporating elements of both narrative and systematic reviews. On the one hand, the systematic aspects are demonstrated by the structured search process, the use of defined keywords, and the clear inclusion criteria for article selection. On the other hand, the narrative approach is evident in the tailored organization and storyline that aligns with the objectives of the review, allowing for a practical and structured guide to Monte Carlo methodologies in the field of light-skin interaction. By blending these two approaches, this review aims to provide a comprehensive yet focused perspective on the current advancements and challenges in the field. Article Selection and Inclusion Criteria The initial list of articles encompassed experimental, theoretical, and hybrid studies, covering a wide range of research methodologies. Articles were included if they satisfied the following criteria: • Relevance : The study had to involve Monte Carlo simulations applied to light-skin interactions, with a clear focus on therapeutic or diagnostic applications. • Recency : Priority was given to studies published within the last 10–15 years to ensure the review reflects the most current developments. • Uniqueness : Articles were filtered to avoid redundancy, with representative studies chosen to highlight unique methodologies or applications. When multiple studies addressed similar approaches, the most comprehensive or impactful study was selected. • Quality : While most references were from high-impact journals, certain studies from lower-ranked journals were included if they provided novel insights or covered applications not discussed in higher-impact studies. • Categorization and Organization The selected articles were categorized based on their application focus to align with the structure of this review: Therapeutic Dermatology Applications : Articles addressing photodynamic therapy, photothermal therapy, and radiotherapy were categorized under therapeutic dermatology topics. Cancer Research Applications : Studies focusing on diagnostics and treatments based on the skin tissue constituent variations, vascular changes, and morphological alterations were grouped under cancer research applications. The selected articles were reviewed to extract specific information related to light-skin interactions. Key findings from this analysis are described below, with relevant visualizations provided to highlight trends and insights. Spectral Range: The spectral ranges reported in the reviewed studies were analyzed to identify the most commonly studied wavelengths. Figure 1 shows the frequency with which different spectral ranges are reported in the research literature in the study of light-skin interaction. This highlights the most commonly studied wavelengths, underscoring their importance in various applications discussed in subsequent sections. Penetration Depth: The relationship between wavelength and penetration depth was examined to determine how different wavelengths interact with skin layers. As shown in Figure 1: This histogram represents the frequency with which different spectral ranges are reported in research literature. The x-axis shows the spectral ranges in nanometers, while the y-axis indicates the number of publications mentioning each range, highlighting the most commonly studied wavelengths. Figure 2, our collected data highlights the relationship between penetration depth and spectral range, demonstrating the varying depths of penetration across different wavelengths. Monte Carlo Implementations: The reviewed articles employed various MC models for simulating light-skin interactions, (See Figures 3 and 4). These models are instrumental in bridging theoretical simulations with clinical applications, ensuring accurate representation of complex biological processes. Radiation Types and Monte Carlo Implementations in Skin Therapy The selection of MC simulation implementations is closely tied to the type of radiation employed in skin therapy studies, as each radiation type interacts differently with skin tissue and requires tailored computational modeling. Ionizing and Non-Ionizing Radiation: Characteristics and Modeling Radiations used in skin therapy are broadly categorized into ionizing and non-ionizing types based on their energy levels and physical interactions with matter: Non-Ionizing Radiation: Encompasses visible, near-infrared (NIR), and ultraviolet (UV) light. Non-ionizing radiation lacks the energy to ionize atoms but interacts through excitation or vibrational energy transfer. These properties make it integral Figure 2: Data points and trend line showing the relationship between penetration depth and spectral range based on our collected data. This graph is generated from a limited number of data points extracted from various research papers, showing the observed trend in penetration depth across different wavelengths. to photodynamic therapy and photothermal therapy, which rely on localized light-skin interactions for disease treatment [35, 36]. Ionizing Radiation: Includes high-energy x-rays, gamma rays, and charged particles, capable of displacing electrons and causing ionization. This type of radiation is fundamental to radiotherapy, where it targets and damages cancerous cells at the molecular level [37, 38]. Accurately modeling these interactions requires MC codes optimized for the specific characteristics of each radiation type. 1. Monte Carlo Implementations for Different Radiation Types MC implementations cater to the unique demands of ionizing and non-ionizing radiations: 2. Non-Ionizing Radiation: Implementations like MCX [39–42], MMC [43, 44], and CudaMCML [45–47] excel in modeling light transport in the visible and NIR spectral ranges. These tools are widely used in PDT and PTT, where precise light distribution modeling is crucial for effective treatment. 3. Ionizing Radiation: Implementations such as MCNP [48], Geant4 [49, 50], EGSnrc [51], Penelope [52] and FLUKA [53, 54] are designed for simulating the interactions of high-energy radiations like x-rays and protons. They are essential for accurate dose calculations in radiotherapy. However an implementation like Geant4 is versatile and capable of handling both ionizing and non-ionizing radiations. Figures 3 and 4 illustrate the prevalence and proportional usage of different Monte Carlo model implementations in the literature. The histogram (Figure 3) shows the frequency of various MC implementations across different years, highlighting the evolution and trends in their application. The pie chart (Figure 4) provides a clear view of the proportional usage of each MC model, with the ’Other’ category encompassing models that have been used only once in the reviewed literature. The dominance of using Geant4 for the simulations, can be attributed to its dual capability to model both ionizing and non-ionizing radiations. This versatility makes it indispensable for comprehensive studies covering multiple therapeutic modalities. Figure 3: This histogram illustrates the prevalence of various Monte Carlo implementations across different years. The implementations are ordered by their total usage frequency, with each color-coded bar segment representing the number of publications in a specific year that employed each implementation. While MC simulations are widely regarded for their accuracy and adaptability in modeling light-skin interactions, alternative computational methods such as the finite element method (FEM) and diffusion approximation (DA) also play important roles. Each approach has distinct advantages and limitations, making them suitable for different applications. A clear comparison highlights why MC simulations are the preferred choice for light-skin interaction studies in this review. A Comparison of Monte Carlo Simulation with FEM and Diffusion Models Monte Carlo Simulations: MC simulations are considered the benchmark method for modeling light transport in highly scattering media due to their statistical accuracy Figure 4: This pie chart displays the proportional usage of different Monte Carlo model implementations in the literature. The ’Other’ category aggregates lesser-used models, providing a comprehensive view of the diversity and dominance of specific methodologies in the field. and flexibility. They work by simulating the random trajectories of individual photon packets, accounting for absorption, scattering, and interactions with the tissue boundaries. This particle-based approach makes MC simulations uniquely capable of handling complex tissue heterogeneities, such as layered structures and chromophore variations, with high fidelity. However, the primary limitation of MC simulations is their computational cost. Achieving high precision requires simulating a large number of photon packets, which can be time-consuming, particularly for high-resolution models. Recent advancements, such as GPU-accelerated implementations, have significantly mitigated this challenge, improving computational efficiency without sacrificing accuracy [25, 26, 46, 55, 56]. Finite Element Method: FEM solves the radiative transfer equation (RTE) by discretizing the tissue domain into smaller mesh-based elements, such as tetrahedrons or hexahedrons [57]. This method is particularly well-suited for modeling light propagation in tissues with smooth, well-defined boundaries and lower scattering coefficients. FEM excels in steady-state and time-dependent problems, offering high accuracy when the physical scenario aligns with its underlying assumptions. Advancements in meshing techniques and higher- order elements have improved its applicability to more complex geometries [58–63]. However, FEM faces significant challenges when applied to highly scattering or heterogeneous media, such as biological tissues. The multiple scattering events and irregular light paths in skin layers with varying optical properties can result in computational challenges and reduced accuracy. Furthermore, the process of meshing and solving large-scale systems of equations requires substantial computational resources, limiting FEM’s practicality for modeling the complex and dynamic structure of human skin [58, 64–67]. Diffusion Approximation: DA simplifies the RTE by assuming that light transport behaves as a diffusion process, which is valid only in highly scattering media where absorption is negligible. Its computational efficiency makes DA attractive for bulk tissue regions and applications where directional information is less critical, such as near-infrared spectroscopy (NIRS) and diffuse optical imaging [68, 69]. However, DA is inherently limited to near tissue boundaries or in regions with low scattering, such as superficial skin layers or interfaces. The assumptions of isotropy and homogeneity break down in these scenarios, leading to inaccurate results. This makes DA unsuitable for modeling thin tissue layers, optical contrasts, or localized light-tissue interactions [70, 71]. Why Monte Carlo Simulations? Given the limitations of FEM and DA in handling the complexities of biological tissues, MC simulations remain the preferred method for accurately modeling light propagation in skin. Their strengths include: • Handling complex tissue heterogeneities , such as layered structures, chromophore variations, and vascular architectures. • Maintaining high accuracy in highly scattering media, even near boundaries and interfaces where other methods fail. • Supporting arbitrary geometries and optical properties without relying on restrictive assumptions like isotropy or homogeneity. These capabilities make MC simulations indispensable for applications requiring precision, such as photodynamic therapy, photothermal therapy, and diagnostic imaging. While computationally demanding, advancements in parallel computing, GPU acceleration [40, 72, 73], and machine learning-based techniques have significantly improved their efficiency (See Section 5). With their unmatched precision, adaptability, and ongoing computational progress, MC simulations are recognized as the premier methodology for light-skin interaction studies. Building on this foundation, the next section delves into the fundamental principles of light propagation in skin tissue, offering the theoretical framework necessary to contextualize the parameters and methodologies discussed here. Fundamentals of Light Propagation in Skin Tissue Exploring the dynamics of how light interacts with skin tissue uncovers essential queries crucial for enhancing therapeutic and oncological treatments. How does the interplay of light within skin layers affect the precision of diagnosis and effectiveness of treatment strategies? What processes dictate the penetration and spread of light, making them pivotal in accurately aiming at affected parts of the tissue? Additionally, given the range of reactions light can provoke, from heating to stimulating cellular repair, how can we adjust these interactions to optimize therapeutic gains while minimizing risks? Addressing these questions is vital, as it forms the foundation for leveraging light-based therapies to innovate and improve dermatological treatments. Basic Principles of Light-Skin Interaction Absorption dynamics in skin tissue Absorption is the process by which light energy is taken up by molecules and converted into other forms of energy, such as heat. The absorption dynamics in skin tissue are influ- enced by various chromophores, including melanin, hemoglobin, and DNA [15, 74, 75]. These chromophores have distinct absorption spectra and play a crucial role in the absorption and scattering of light in skin [76, 77]. The concentration and distribution of these chromophores can vary based on factors such as skin type and depth [78]. Understanding the influence of these chromophores on light absorption is essential for the development of effective photonics- based therapeutic techniques in medicine (photomedicine) for dermatological applications. Scattering phenomena and tissue heterogeneity Light scattering in skin tissue is intricately linked to the microstructural variations within cells and the extracellular matrix. This scattering is primarily influenced by differences in the size, shape, and refractive indices of cellular components, notably the nuclei and organelles, as well as extracellular matrix molecules such as collagen fibers. The sensitivity of scattering to these parameters makes it a crucial factor in identifying cellular changes, such as those occurring as a result of pathology [79, 80]. The fractal-like architecture of skin tissue at the nanoscale alters the scattering patterns, affecting how deeply light can penetrate and be absorbed by skin. This fractal nature refers to the size distribution of particles within cells and tissues, where the relative ratio of larger to smaller particles remains consistent across different magnification levels [81]. This property is critical for evaluating the effectiveness and safety of light-based treatments, as it dictates the distribution of light within different skin tissue layers. Research by Tuchin and Popp emphasizes that tissue heterogeneity not only influences the scattering of light, but also aids in distinguishing between different tissue types and condi- tions through differences in optical properties [82,83]. Understanding these nuances enhances the ability to leverage light-based methods for precise diagnostic and therapeutic interven- tions, facilitating improvements in treatment accuracy and patient outcomes. Reflection and refraction at the tissue interfaces Reflection and refraction at biological tissue interfaces are fundamental optical phenomena that directly influence light propagation and intensity. These processes are governed by the refractive index and the structural configuration of the tissues, making them crucial for assessing tissue properties and pathological changes. Reflectance measurements, for instance, can indicate the tissue health as they vary with changes in optical properties, which may correlate with different pathological states [84, 85]. The roughness of tissue interfaces modifies the scattering and distribution of light - this is crucial for accurate modeling of light propagation in layered tissues [12]. Furthermore, the Mueller matrix provides a comprehensive method to document the polarization changes induced by reflection and refraction, enhancing diagnostic capabilities by detailing these interactions [86]. Near- infrared techniques that utilize specific refraction properties significantly improve imaging resolution and penetration depth, enabling more effective probing of deeper tissue layers [87]. These insights are pivotal for advancing diagnostic and therapeutic technologies, demonstrating the critical role of optical phenomena at tissue interfaces in biophotonics. Optical Properties and Structural Considerations of Skin Key optical properties defining light-skin interaction The optical properties of the skin layers, such as the stratum corneum, epidermis, and dermis, are characterized by specific optical coefficients that determine how light interacts with the skin. The scattering coefficient ( µ s ) measures the extent to which light is scattered per unit path length in the tissue. It is generally much higher than the absorption coefficient ( µ a ), which quantifies the amount of light absorbed per unit path length [88, 89]. The anisotropy factor g , commonly used to describe the directionality of scattering, is typically derived from the Henyey-Greenstein (HG) phase function, a standard model for simulating the angular distribution of scattered light. It ranges from -1 (indicating perfect backward scattering) to +1 (indicating perfect forward scattering). In skin tissues, g is high and positive, reflect- ing strong forward scattering [88, 90]. However, it has been shown that the HG function underestimates the contribution of backscattered light under certain conditions (e.g., in ap- plications with small source–detector separations) [91]. Alternative phase functions, such as the Gegenbauer kernel and the Mie phase function can be more appropriate in specific simulation scenarios [92]. Variability in these coefficients, such as differences in µ a across ethnic groups in the epi- dermis, highlights the complex nature of light-skin interaction [93]. Continued research is necessary to define more precisely the range of these coefficients for healthy skin [89]. Skin multilayer architecture and optical variability The multilayer architecture of the skin plays a crucial role in the modulation of light behavior. The epidermis primarily absorbs light due to melanin concentration, which crucially deter- mines photon penetration depth [18]. Beneath this layer, the dermis scatters and absorbs light, affecting the overall light reflectance and scattering properties [94, 95]. The subcuta- neous layer, or hypodermis, primarily composed of fat, interacts with light differently due to its lower scattering and absorption compared to the upper layers. This layer’s chromophores and scatterers, such as lipids, impact the light path and its eventual exit from the tissue [96]. The heterogeneity between these layers’ interfaces further modifies how light is distributed, introducing additional complexity to modeling efforts [97]. Additionally, age-related changes in optical properties within these layers, such as decreased collagen density, reduced hydra- tion, and increased skin roughness, necessitate adjustments in both diagnostic imaging and therapeutic applications, as they can alter absorption and scattering behaviors [98]. Wavelength-dependent behavior of light in skin The interaction of light with skin varies significantly across different wavelengths. Wavelength-specific absorption of light by skin chromophores dictates not only the depth of penetration but also the biological response. For instance, hemoglobin exhibits distinct absorption peaks in the visible spectrum, which significantly influence the optical properties of the dermis [76]. In contrast, melanin displays a broad and monotonically decreasing absorption profile across the visible range, without sharp peaks [88]. This wavelength dependency is critical in applications such as selective photothermolysis, where targeted wavelengths are used to achieve precise thermal effects without damaging surrounding tissues [15]. Furthermore, the variations of light scattering with wavelength affects how light is redistributed within the skin. Shorter wavelengths tend to scatter more than longer wavelengths, which impacts the visualization of skin structures in imaging applications [99]. Environmental factors such as humidity, temperature, and exposure to ultraviolet (UV) radiation also play a role, altering how skin interacts with light across different conditions and leading to varied spectral signatures [100]. The perceived color of skin, a result of the combined effects of absorption, scattering, and reflection, is also wavelength-dependent. This property is essential for non-invasive diagnostic tools that rely on detecting color changes indicative of underlying health conditions [101]. Understanding these wavelength-specific behaviors is vital for advancing photodynamic therapy and improving the accuracy of skin cancer detection methods [102]. Biophysical Effects of Light Propagation in Skin Penetration depth and targeted therapeutic interventions The depth of penetration of light into skin varies with wavelength, with longer wave- lengths penetrating deeper [103, 104]. Factors such as illumination geometry, skin tone, and the use of optical clearing agents can also influence light-skin interaction and penetration depth [104–106]. Techniques such as biocompatible microneedle waveguides integrated into optoelectronic devices can enhance UV light delivery, achieving significant improvements in light penetration and reducing phototoxicity [107]. Laser-assisted drug delivery leverages on the penetrative properties of specific laser wavelengths to facilitate deeper drug absorption into the skin by creating micro-channels or temporary disruptions, enhancing the delivery and effectiveness of therapeutic agents [108]. The use of low-level lasers and specific beam shapes can also affect the depth of penetration in dermatological treatments [109, 110]. Thermal effects and selective photothermolysis Selective photothermolysis leverages heat generation as a result of energy deposition when photons are absorbed to target and destroy pathological tissues. This method achieves selec- tive thermal damage by exploiting the absorption characteristics of specific chromophores in the tissue [111]. For example, selective photothermolysis is effectively applied in the removal of cutaneous vasculopathies and tattoos by targeting blood vessels and pigments [112]. In- frared vibrational bands can be used to selectively heat and break down lipid-rich tissues, making it a potential treatment for conditions involving excessive fat deposits [113]. The use of endogenous chromophores, such as melanin and hemoglobin, and exogenous agents such as gold nanoparticles, dyes, and photosensitizers enhances the specificity and efficiency of thermal effects, allowing precise targeting of pathological areas [114–116]. Additionally, optical skin clearing techniques can further improve the effectiveness of selective photother- molysis by reducing scattering and increasing light penetration [116]. Photobiomodulation and cellular response activation A range of light parameters have been found to stimulate healing and cellular repair mechanisms. Blue light, particularly at a low energy density, has been shown to promote cell proliferation and migration [117]. The effectiveness of low-level light therapy (LLLT) is influenced by the energy density and intensity of the light [118]. Specific wavelengths, such as 810 nm, have been found to be effective in promoting healing [119]. The use of light-emitting diodes (LEDs) has also been explored, with blue light decreasing proliferation and red light promoting it [120]. These findings collectively suggest that the choice of light parameters, including energy density, intensity, and wavelength, can significantly impact the effectiveness of phototherapy in stimulating healing and cellular repair mechanisms. Monte Carlo Simulation Technique Foundational principles of Monte Carlo simulations in light-tissue modeling Monte Carlo simulations are a powerful tool for modeling light transport in tissues, particu- larly in complex and heterogeneous environments where other methods may fail [121]. These simulations involve tracking individual photon packets and can be used to model a range of light-tissue interaction, including fluorescence [55]. To improve the efficiency of these simulations, various methods have been developed, such as GPU-based parallel implementa- tions [122] and shape-based codes [123]. Recent advances in Monte Carlo simulations have further expanded their applications, including whole mouse body simulations for fluorescence imaging and eye modeling for blood vessel imaging [26]. These simulations have also been used to study the interaction of light with biological tissue in medical hyperspectral imaging applications [24]. Despite these advancements, the field continues to evolve, with ongoing research focusing on new measurement geometries and recording techniques [25]. Modeling light distribution in multilayered skin with MC simulations Monte Carlo simulations have been widely utilized to model light distribution in multilay- ered skin, demonstrating their capability to accurately represent complex tissue structures. A notable example is the MCML (Monte Carlo Modeling of Light transport in Multi-layered tissues) model, developed by Wang et al. [124], which allows for dynamic data allocation to vary tissue layers and grid elements at runtime. This model optimizes grid coordinates and provides robust simulations verified against other theories, making it a valuable tool for light-tissue interaction studies in complex skin structures. Hybrid models combining MC simulations with other techniques have been developed to analyze light propagation in skin layers, enhancing the accuracy of these simulations [125]. Artificial neural networks trained with MC simulation data have been proposed to extract chromophore information from multi-layered skin tissue, improving the precision of optical diagnostics [126]. Studies us- ing multilayered phantoms and detailed skin models have simulated skin reflectance spectra and investigated the effects of laser irradiation, providing insights into therapeutic applica- tions [127, 128]. Analytical models have been applied to calculate the optical properties of the epidermis, contributing to a deeper understanding of light-tissue interaction [129]. An improved tetrahedron-based MC method has been introduced to simulate light propagation in complex skin tissues, particularly addressing the shading effect of cross-bridge blood ves- sels [130]. Additionally, layered heterogeneous spectral reflectance models have captured the inter-scattering of light among skin layers, offering a comprehensive approach to studying light distribution in human skin [131]. These advancements highlight the effectiveness of MC simulations in modeling the layered structure of skin and their significant implications for both diagnostic and therapeutic applications. Monte Carlo simulations in clinical applications Monte Carlo simulations have significantly advanced diagnostic and therapeutic approaches in clinical applications. In particle therapy, these simulations are essential for accurate dose calculations, facility design, and range monitoring [27]. The GATE Monte Carlo toolkit has enabled precise dose quantification in molecular imaging and radiotherapy [132]. Advancements in high-performance computing and simulation algorithms have increased the practicality of these simulations for clinical use [133]. In radiation medicine, Monte Carlo techniques have optimized imaging protocols and correction methods, enhancing the accuracy of radiotherapy simulations [134]. These simulations also aid in predicting particle interactions with tissue, improving treatment outcomes and reducing treatment volumes [135]. Integration of Monte Carlo codes in therapeutic applications facilitates the analysis of dose delivery and range uncertainties, crucial for patient dose distribution assessments [136]. In the next section, we will delve deeper into these applications. Results Overview of MC Simulation in therapeutic Dermatology Photodynamic therapy Photodynamic therapy (PDT) is a common treatment method for various diseases and is particularly efficient in the treatment of non- melanoma skin cancer and precancerous lesions due to its minimal invasiveness and low systemic toxicity. The non-toxic sensitizer used in PDT becomes toxic only upon illumination, allowing for targeted treatment with reduced side effects compared to traditional therapies [137–143]. Central to PDT is the application of a photosensitizer, which, upon activation by light in the visible to near-infrared spectral range, transfers energy to oxygen in the tissue, generating cytotoxic reactive oxygen species, primarily singlet oxygen, leading to targeted cell death [137, 144–146]. The selectivity of this method, coupled with its ability to spare surrounding healthy tissue, marks its significant advantage, alongside the customization of treatment parameters such as photosensitizer dose and light irradiance to optimize therapeutic outcomes [137, 147–149]. However, the efficacy of PDT is contingent upon adequate tissue oxygenation and is primarily suited for superficial lesions due to the limited penetration depth of therapeutic light [148]. Recent advances in Monte Carlo simulations have shown significant potential for enhancing PDT treatment planning by enabling accurate modeling of the light distribution to support rapid and precise dose computation in complex treatment scenarios [150]. To further advance the precision of PDT, Quintanar et al. [151] introduced an integrated system that administers treatment while monitoring tissue oxygen saturation—a crucial factor for PDT success. Calibrated through Monte Carlo simulations that adapt to varying skin thickness and melanin content, this system enables real-time tuning of PDT parameters, potentially extending its effectiveness to more diverse skin conditions. In another study focused on photosensitizer behavior, Van et al. [152] utilized Monte Carlo simulations alongside single-fiber fluorescence spectroscopy to dissect the intrinsic fluorescence spectra of chlorin e6 and Bremachlorin in vivo . This study was pivotal in characterizing the spectral variations and determining the distinct behaviors of these photosensitizers, which are essential for optimizing Bremachlorin-based PDT protocols. Simulations played a critical role in refining the spectral analysis by compensating for the influence of the optical properties of the tissue, thereby paving the way for more effective and tailored PDT treatments. In a series of studies, Campbell et al. explored various facets of PDT using Monte Carlo simulations to refine treatment methodologies. In one study [153], they delved into the impact of aging and skin type on PDT effectiveness, revealing that daylight could be a practical light source for treating superficial lesions, highlighting the potential for personalized treatment protocols. Another investigation [154] contrasted daylight-activated PDT with traditional methods, emphasizing the need for precise calculations in treatment planning and suggesting daylight PDT as a feasible option for superficial conditions, albeit with a slower treatment pace. In another study, in order to enhance PDT effectiveness for nonmelanoma skin cancers, Fanjul-V´elez et al. [155] introduced a predictive model integrating Low Intensity Laser Therapy (LILT) with PDT. Using Monte Carlo simulations, this model optimizes light propagation and dosimetry, accommodating various tumor types and geometries. The approach improves oxygenation and treatment planning, potentially reducing tumor recurrence and enhancing clinical outcomes. Further research by the same group [156] tackled the non-uniform distribution of Protoporphyrin IX in PDT, proposing a sophisticated simulation model to better predict treatment outcomes. This model accounted for the dynamic interaction between the applied prodrug and its conversion, aiming to enhance the accuracy of dosimetry in PDT. Additionally, they examined the efficacy of PDT in tumor treatment by comparing different tumor models [157]. This comparison underscored the significance of tumor structure in determining light penetration and treatment efficiency, necessitating the need for more complex models to accurately simulate PDT interaction with tumor tissues. In a comprehensive study, Lopez et al. [158] developed a Monte Carlo simulation to detail the distribution of crucial PDT components—ground state oxygen, singlet oxygen, and the photosensitizer PpIX—introducing the tumor reactive singlet oxygen (TRSO) metric for enhanced treatment dosimetry. This model, corroborated by experimental data, underscores the efficiency of PDT for tumors below certain sizes, positing the TRSO as a promising guide for clinical dosimetry and a step forward in realizing personalized PDT protocols. Aiming to revolutionize PDT for cancer, a novel study introduces an innovative wavelet- based genetic algorithm search (WGAS) to enhance the efficiency of Monte Carlo method for computing light dose distributions [150]. By significantly reducing computation times while maintaining accuracy, this approach paves the way for more practical and precise PDT planning, promising a leap forward in targeted cancer treatments with minimized impact on healthy tissues. To improve PDT accuracy for skin cancer, Zhao et al. [159] developed a rapid photon fluence calculation method using perturbation theory with Monte Carlo simulations. This approach allows real-time adjustment of PDT parameters, potentially enhancing treatment efficacy and minimizing harm to normal tissues. LaRochelle et al. [160] utilized Monte Carlo simulations to refine PpIX-PDT by estimating light fluence rates within skin layers. This led to lookup tables for determining optimal treatment times based on light source and skin depth, enhancing treatment precision for skin cancers. Addressing a pivotal shift in the approach to endogenous PDT, this research zeroes in on optimizing the use of coproporphyrin III for combating Staphylococcus aureus infections [161]. Leveraging Monte Carlo simulations, the study illuminates the potential of specific wavelengths tailored to coproporphyrin III’s absorption peaks, offering a promising avenue for more effective skin and soft tissue infection treatments. This nuanced understanding paves the way for employing multiplexed wavelengths, potentially amplifying the efficacy of PDT for bacterial eradication in skin-related applications. To enhance PDT precision for melanoma, Doronin et al. [162] developed a structured illumination technique, optimized via Monte Carlo simulations, to reduce overheating and improve treatment efficacy. This method refines light delivery, potentially enhancing therapeutic outcomes across diverse melanoma cases. To enhance PDT precision in melanoma treatment, Vasilieva et al. [163] introduced an advanced 3D multilayer model of melanoma-involved human skin using Monte Carlo simulations and machine learning. Their study systematically varied melanin and blood concentrations to simulate light propagation, integrating volumetric renderings and a bidirectional surface scattering reflectance distribution function (BSSRDF)-based framework to evaluate subsurface scattering effects. The approach improves understanding of light–tissue interactions and supports personalized treatment planning in melanoma therapy. A summary of the key points discussed in this subsection is presented in Table 1. Table 1: Overview of research papers utilizing Monte Carlo simulations in PDT 1 Elena Vasilieva 2025 Proc. SPIE 400 to 1000 Developing a 3D melanoma model within layered skin for PDT optimization using MC simulation and ML; Investigating light propagation influenced by melanin, blood, and tumor geometry Simulating photon propagation using a custom MC model and BSSRDF; generating training data for ML-based prediction of PDT signal behavior 2 Alexander Doronin 2024 Biomed. Opt. Express 600, 800 Improving PDT outcomes for melanoma with computational simulations Modeling light transport and optimizing structural arrangements in PDT 3 Alec B. Walter 2020 Photodiagnosis Photodyn. Ther. 350 to 750 Enhancing PDT efficacy against Staphylococcus aureus by refining optical parameters, utilizing endogenous coproporphyrin III Estimating fluence rates and bacterial reductions as a function of depth; Predicting the effectiveness of light sources in treating skin infections 4 Kuanxin Zhao 2018 Photonics Dermatol. Plast. Surg. 633 Noninvasive treatment of early-stage skin cancers using PDT Gold standard for photon fluence calculation, optimizing dosimetry 5 Ethan P.M. LaRochelle 2019 Photodiagnosis Photodyn. Ther. 350 to 900 Estimating light fluence in tissue for multiple light sources used in PpIX-PDT Providing clinically relevant methods to estimate light fluence rates at depth in tissue 6 Meenaakshi Sund- hari R P 2018 Asian Pac. J. Cancer Prev. Improving the efficiency and accuracy of light dose distribution computation for PDT Simulating light dose distribution for accurate PDT treatment planning 7 Naiyan Huang 2014 Chin. Opt. Lett. 532 PDT efficacy in Port-Wine Stain (PWS) lesions; optimizing laser distribution Simulating laser distribution and singlet oxygen generation in the tissue 8 N. Lopez 2016 J. Photochem. Photobiol. B 630 Improving dosimetry protocols and treatment planning in PDT; Proposing a model for simu- lating spatial and temporal distribution of oxygen and photosensitizer in PDT Computing light fluence in tissue 9 C. Louise Camp- bell 2016 SPIE Photonics West - Photonic Therapeutics Diagn. XII 350 to 700 (405, 540, 630) Investigating PDT using a 3D MCRT model for better representation of light interaction with complex tumor structures Simulating light distribution, scattering, and absorption events in skin tissue during PDT 10 C. Louise Camp- bell 2016 Phys. Med. Biol. 350 to 700 Modeling the PpIX production considering incu- bation time and treatment modality Modeling the light distribution and its interaction with PpIX 11 C. Louise Camp- bell 2015 Phys. Med. Biol. 350 to 700 Investigating the potential and limitations of daylight activated PDT for treating superficial skin lesions Simulating light propagation and photodynamic dose distribution 12 F. Fanjul-V´elez 2015 IEEE Trans. Biomed. Eng. 633 Combination of LILT and PDT for enhanced treatment of nonmelanoma skin cancers Modeling light propagation and optimizing treat- ment parameters 13 C. Louise Camp- bell 2015 Proc. SPIE 350 to 700 Investigating the effects of ageing and skin type on PDT Simulating light distribution and energy deposi- tion in skin tissue during PDT 14 Floor van Leeuwen-van Zaane 2014 J. Biomed. Opt. 400 to 800 Quantifying intrinsic fluorescence for photosensi- tizer optimization in PDT Correcting the influence of tissue optical properties 15 L. Quintanar 2013 8th Iberoamerican Opt. 11th Latin Am. Meet. Lasers, Appl. Meet. Opt., 630, 660, 940 Creating a PDT irradiation system incorporating a fiber-optic sensor to monitor tissue oxygen levels Modeling the pulse oximeter to study the behavior of the sensor with changes in skin thickness and melanin content Photothermal therapy Photothermal therapy (PTT) is an emerging modality that leverages the ability of certain nanomaterials, known as photothermal agents (PTAs), to absorb light energy and convert it into localized heat, resulting in targeted thermal destruc- tion of cells or tissues [164]. The therapeutic potential of PTT stems from the selective accumulation of PTAs, such as carbon-based [165–167], gold-based [168, 169], or organic dye-based [170–172] nanoparticles, within the targeted region, followed by their irradiation with near-infrared (NIR) light, which penetrates deeper into biological tissues [173, 174]. This localized photothermal effect can effectively ablate malignant tumors while minimizing collateral damage to surrounding healthy tissues. PTT has also shown promise in various other medical applications, including antimicrobial therapy [175–177] and tissue regenera- tion [178, 179]. However, clinical translation of PTT is currently hindered by challenges in determining optimal treatment parameters, such as the type and concentration of PTAs, light source characteristics, and irradiation protocols, to achieve precise spatiotemporal con- trol over the photothermal effect [180, 181]. To address these limitations, recent efforts have focused on developing computational models, including Monte Carlo simulations, to better understand and predict the complex light-tissue interaction involved in PTT, thereby en- abling the refinement and optimization of treatment strategies. Didychuk et al. used Monte Carlo simulations to validate the effectiveness of AuNR-assisted PTT for selective cancer cell and tumor destruction in skin tissue-like phantoms [182]. Grosges et al. introduced a Monte Carlo method to design nanoparticles, confirming that hollow nanospheres have the best burning efficiency for PTT applications [183]. Manuchehrabadi et al. investigated the effect of gold nanorod concentrations on laser energy absorption using a Monte Carlo simulation, showing that higher concentrations led to increased absorption and potential thermal damage [184]. Daoudi et al. presented an add-on to a photoacoustic imager to monitor temperature changes during PTT using speed of sound tomography [185]. Manuchehrabadi et al. developed a computational simulation integrating the Arrhenius integral with a heat transfer model to identify heating protocols for treating prostate tumors using transurethral laser photothermal therapy [186]. Gong et al. performed an off-lattice Monte Carlo simulation to investigate the impact of SWNT characteristics and interfacial modification on heating efficiency in cancer PTT [187]. Ren et al. utilized the Monte Carlo method and bioheat transfer equation to study the effects of period heating, GNP volume fraction, and laser irradiation area on heat distribution in PTT [188]. Zhang et al. compared Monte Carlo and diffuse approximation methods, highlighting the importance of real-time temperature monitoring to minimize thermal damage in PTT [189]. Xu et al. employed a mathematical simulation model and Monte Carlo method to investigate temperature distribution during PTT, validating results with experimental data [190]. Wang et al. occupied a Monte Carlo approach to study the impact of graphene nanosheet characteristics and interfacial thermal resistance on temperature changes in cancer cells and healthy tissue during PTT [191]. Hsieh et al. verified through Monte Carlo simulations and experiments that high-intensity focused ultrasound (HIFU) can improve light delivery efficiency for PTT and enhance photoacoustic signal [192]. Kim et al. utilized the Monte Carlo method to investigate the optimal treatment effect based on tumor depth, laser intensity, and gold nanorod volume fraction in PTT [193, 194]. Sun et al. used Monte Carlo models to simulate photon transport and develop heat transfer models, comparing the photothermal effects of moxibustion and laser irradiation on biological tissue [195]. Kim et al. applied the Monte Carlo method to evaluate the optimal treatment effect of PTT with direct injection of gold nanoparticles into tumor tissue [196]. Wachsmuth et al. demonstrated the efficacy of gold nanostars in optimizing laser interstitial thermal therapy for treating intracranial tumors using Monte Carlo simulations and in vivo models [197]. Kim et al. [198] developed a model using Monte Carlo simulations and finite-difference time- domain methods to predict photothermal damage during skin laser treatment. This model, incorporating skin-specific data, accurately predicts damage depth and area, improving precision in laser treatment planning for dermatological applications. To study photothermal effects, Reddy et al. [199] developed a multiphysics model using Monte Carlo simulations and Pennes’ bioheat equation. The model assesses terahertz and optical radiation effects on temperature rise, optimizing PTT to deliver precise heat and minimize tissue damage. In order to enhance selective thermal damage in PTT, Zerakni et al. [200] employed Monte Carlo simulations via the Geant4 toolkit to study the effect of Gold nanoparticles (GNPs) on light absorption in skin tumors. Their findings showed that even low GNP concentrations notably increased absorption in the 900–1200 nm range, enabling more effective tumor heating under polychromatic laser exposure. Table 2 provides an overview of the main details covered in this subsection. Table 2: Research papers on Monte Carlo simulations in photothermal therapy 1 F. Zerakni 2024 Lasers Med. Sci. 900–1800 Evaluating the role of Gold nanoparticles in en- hancing laser absorption and thermal response in skin tumors under polychromatic laser irradiation for improved PTT outcomes Modeling photon transport, Mie scattering, and thermal effects with varying GNP concentrations 2 Hyo-Jin Kim 2023 J. Comput. Des. Eng. 900 to 1300 Predicting photothermal damage during skin laser treatments Calculating light distribution within the skin 3 Innem V. A. K. Reddy 2023 Sci. Rep. 130 GHz (2.3 mm) to 1 THz (0.3 mm) and 1030 nm Investigating photothermal effects of terahertz and near-infrared radiation on human tissues Providing accurate predictions of EM wave propagation and heating effects 4 Lucas Wachsmuth 2023 Neuro-Oncol. Adv. 1064 Optimizing LITT for the treatment of intracranial tumors by improving the efficiency and safety of the platform Demonstrating the ability of GNS to accelerate and focus thermal energy distributions 5 Donggyu Kim 2023 Pharmaceutics NIR Optimizing PTT conditions for effective apoptosis and minimal thermal damage Calculating light absorption and scattering, deter- mining temperature distribution 6 Chao Sun 2021 Int. J. Therm. Sci. 808 and 35,000 Evaluating the photothermal impacts of moxibus- tion therapy versus laser irradiation on biological tissues Simulating photon transport and energy deposi- tion in tissue 7 Donghyuk Kim 2021 Appl. Sci. NIR Deriving optimal PTT conditions to maximize apoptosis in tumors while minimizing thermal damage to surrounding normal tissues Modeling photon transport and heat distribution to optimize PTT conditions 8 Donghyuk Kim 2021 Int. J. Mol. Sci. 1064 Identifying the best conditions for triggering apoptosis in tumor cells while reducing thermal harm to nearby healthy tissues Evaluating light absorption and heat transfer in multi-layered skin structures 9 Zong-Han Hsieh 2020 Sci. Rep. 633 Developing a noninvasive method to improve light delivery efficiency in biological tissues using HIFU- induced heating tunnel Simulating the HIFU-induced enhancement of light delivery 10 Yijuan Wang 2020 Int. J. Numer. Methods Biomed. Eng. NIR To quantitatively and systematically study cancer PTT using NIR and graphene and suggest ways to optimize treatment Modeling heat transport and temperature distri- bution in cancer PTT 11 Yuanyuan Xu 2019 Theor. Biol. Med. Model. 805 Analyzing temperature patterns in both tumor and adjacent healthy tissues through a mathemat- ical simulation model Simulating light and thermal energy distribution in tissues 12 Xiyang Zhang 2018 Proc. SPIE None Developing a model for monitoring photothermal treatment in real-time and minimizing thermal damage to healthy tissues Simulating the fluence rates of different beam sizes in biological tissue 13 Yatao Ren 2017 Int. J. Heat Mass Transf. 600 to 1300 Investigating the optimal temperature distribution in gold nanoparticle enhanced PTT to prevent overheating and minimize damage to healthy tissues Calculating the heat generation of tissue and GNPs irradiated by laser 14 Feng Gong 2014 Nanotechnology 700 to 1100 Developing a mesoscopic model of cancer PTT using NIR and SWNTs Simulating the effect of SWNT morphologies and dispersions on the heating efficiency in cancer PTT 15 Navid Manuchehrabadi 2014 Int. J. Hyperthermia 808 Developing theoretical models to design treatment protocols using transurethral laser PTT for prostate cancer patients Predicting laser photon propagation and energy deposition in the prostate and tumor regions 16 Khalid Daoudi 2013 J. Biomed. Opt. 532 Monitoring temperature changes in PTT using hybrid imaging techniques Simulating light propagation and estimating absorbed energy 17 Navid Manuchehrabadi 2013 J. Biomech. Eng. 808 Simulating photon propagation in tumors and compare simulated temperatures with experimen- tal measurements Simulating photon propagation, calculating laser energy deposition and examining effects on temperature distribution 18 Thomas Grosges 2011 Biomed. Opt. Express 800 to 1000 To determine and optimize the sensitivity of geometrical and material parameters of nanoshells for PTT Evaluating tolerances on geometrical and material parameters; Optimizing absorption efficiency 19 Candice L Didy- chuk 2009 Nanotechnology 800 Examining distribution of AuNR conversion dur- ing PTT Investigating laser radiant exposure distribution within the gel Radiotherapy Radiotherapy utilizes ionizing radiation to damage cancerous cells and slow tumor growth. External beam radiotherapy is commonly applied for cancer treatment, using high-energy photons (mega electron volt range) to target deep-seated tumors while sparing superficial tissues [201]. However, accurately predicting dose deposition at or near the skin surface remains challenging [202]. Treatment planning systems often provide inaccurate superficial dose estimates due to factors like beam energy, angle, and patient surface contour irregularities [203]. To address these limitations, Monte Carlo simulation has been instrumental for modeling radiotherapy dose deposition in heterogeneous tissues [201, 203]. Novel techniques leveraging Monte Carlo simulation show promise for concentrating dose at specific depths while reducing skin dose, including approaches using orthovoltage x-rays [204], proton minibeams [205], and focusing lenses [201]. Integrated MRI-linear accelerators also enable daily imaging to adapt radiotherapy plans, though electron return effects must be considered [206]. Overall, advancing radiotherapy requires continued innovation in treatment planning and delivery to provide tumor control while sparing normal tissues. In the realm of radiotherapy, particularly for skin cancer treatments, a series of studies have significantly leveraged Monte Carlo simulations to refine and enhance treatment methodologies. These simulations have been instrumental in addressing various challenges and introducing innovations within radiotherapy practices. Yu et al. [207] embarked on an investigation into the effects of lead shields during kilovoltage x-ray treatments, particularly focusing on their impact on radiation dose perturbations. Through meticulous Monte Carlo simulations and experimental methods using EBT2 Gafchromic film, this study elucidated the dose reductions associated with lead shields. These insights are pivotal for ensuring precise dose delivery in superficial cancer treatments, especially on facial regions, thereby enhancing treatment precision and patient safety. Zhang et al. [208, 209] explored the potential of Cˇerenkov emission as a surrogate for superficial dose measurements in tissue during radiotherapy. Their research highlighted the technique’s high spatial resolution and real-time dose assessment capabilities. By comparing Cˇerenkov emission outcomes with conventional dosimetry methods, these studies underscored the method’s adaptability and efficiency in estimating superficial doses. The employment of Monte Carlo simulations further validated the reliability of Cˇerenkov emission in complex treatment scenarios, including oblique beam incidences, suggesting its applicability in optimizing radiotherapy processes. In another study, Abbas et al. [201] delved into the utilization of low-energy focused X-ray beams, facilitated by polycapillary optics, for targeting tumors within soft tissue. Monte Carlo simulations played a crucial role in modeling dose profiles, demonstrating the beams’ potential to concentrate high doses on the target while sparing the skin. This approach marks a significant shift towards minimizing skin damage in radiation therapy, offering a more patient-friendly treatment option. Brost et al. [210] investigated the dependence of the Cerenkov scatter function on various parameters, employing Monte Carlo simulations to derive an analytical equation for the CSF. This study aimed to refine in vivo dosimetry during radiation therapy, emphasizing the importance of accurate CSF material selection. The research focus on skin models underscores its relevance to superficial dosimetry, enhancing our understanding of Cerenkov photons’ behavior within skin tissues. Tobola et al. [211] performed an optimization on proton mesh collimators for eye proton therapy highlighted the necessity of protecting eyelid skin from radiation-induced complications. Through Monte Carlo simulations, the study assessed the depth dose distribution and optimized collimator parameters, emphasizing the importance of considering skin protection in treatment planning. Further studies by Girou [212] and Wang [213] introduced novel methodologies for radiation therapy. Girou et al. introduced a novel optical system utilizing a Laue lens for radia- tion therapy. This system, modeled through Monte Carlo simulations with Geant4, aims to deliver focused beams that spare the skin while concentrating the dose on the target area, potentially revolutionizing treatment approaches. Wang et al. evaluated skin doses from MRI-Linac and conventional linear accelerators, correlating these with in vivo mea- surements to enhance MRI-Linac planning processes. The study emphasized the importance of considering skin dosimetry in MRI-Linac treatments, with Monte Carlo simulations pro- viding accurate predictions of skin dose delivery. Rafiei et al. [214] used Monte Carlo simulations to calculate mass attenuation coefficients for photons below 1 keV in human tissues. This study identifies suitable tissue-equivalent materials, enhancing radiotherapy dose accuracy for low-energy photon applications, and ensuring precise dose delivery with minimal skin damage. To evaluate radiodermatitis, Hao et al. [215] introduced a low-cost hyperspectral imaging system utilizing Monte Carlo simulations. This system assesses erythema by analyzing skin parameters, such as blood volume and oxygen saturation, providing a non-invasive tool for early detection and monitoring. In order to improve dose calculation in Boron Neutron Capture Therapy (BNCT), a type of targeted radiotherapy, Wang et al. [216] introduced a physically constrained Monte Carlo–Neural Network (PCMC–NN) algorithm. This method enhances accuracy and speed while maintaining physical constraints, significantly reducing calculation errors in sensitive areas, making it a reliable tool for clinical BNCT planning. In another study, Barghash et al. [217] employed EGSnrc Monte Carlo simulations to quantify radiation dose enhancement in bone during superficial x-ray therapy with Sensus SRT- 100 beams (50–100 kVp). Their results showed peak bone doses reaching over five times the surface dose, particularly at water–bone interfaces. The findings highlight the clinical relevance of accounting for tissue composition in radiotherapy planning to avoid excessive skeletal dosing. Finally, in a study aimed at expanding the clinical utility of Intraoperative Radiotherapy (IORT) applicators, Lee et al. [218] used Monte Carlo simulations (TOPAS, GEANT4-based) to assess their dosimetric advantages for treating superficial skin lesions such as keloids and lymphoma. Their experimentally validated simulations demonstrated superior surface doses, shallower dose penetration, and sharper penumbras compared to conventional Cerrobend blocks, potentially reducing dose to adjacent normal tissues. Together, these studies underscore the instrumental role of Monte Carlo simulations in ad- vancing radiotherapy techniques, particularly those targeting or affecting the skin. Each research effort contributes to the overarching goal of achieving precise, effective, and safe treatment protocols, marking significant strides in the field of radiotherapy. An organized summary of the studies discussed above is given in Table 3. Table 3: Summary of studies employing Monte Carlo simulations in radiotherapy applications(* Spectral ranges originally given in keV or kV have been converted to nm using standard conversion formulas. The original units are provided in parentheses.) 1 Reham Barghash 2025 J. Appl. Clin. Med. Phys. 0.00827 to 0.0248* (50 to 100 kVp) Quantifying bone dose enhancement during super- ficial x-ray radiotherapy and its implications for treatment planning Modeling SRT-100 x-ray beams and bone dose using EGSnrc for accurate predictions in water and bone phantoms 2 Ui-Seob Lee 2025 Sci. Rep. 4–6 MeV elec- tron beams Evaluating dosimetric advantages of IORT appli- cators for superficial skin lesion treatments com- pared to Cerrobend blocks Monte Carlo simulation (TOPAS/GEANT4) for modeling linear accelerator electron beams and dose validation 3 Yongquan Wang 2024 Med. Phys. None Enhancing BNCT dose calculation speed and accuracy in treatment planning for glioblastoma and other cancers Essential for simulating neutron-photon transport and optimizing treatment plans 4 Shicheng Hao 2023 Biomed. Opt. Express 420 to 660 Development and validation of a low-cost algo- rithm for assessing radiodermatitis Key method for deriving hyperspectral data and physical characteristics 5 Mustafa Moham- mad Rafiei 2022 Biomed. Phys. Eng. Express 1.24 to 12.4* (0.1 to 1 keV) Study of mass attenuation coefficients and tissue equivalency for radiation protection and dosimetry Key method for estimating mass attenuation coefficients in various tissues 6 Michael H. Wang 2022 Technol. Cancer Res. Treat. None To evaluate skin dose modeled from MR-Linac and conventional Linac, and correlate with in vivo measurements To accurately predict dose distribution in MR- Linac treatment plans 7 David Girou 2021 Phys. Med. Biol. 0.00413 to 0.0155*(80 to 300 keV) Design and model a Laue lens for radiation therapy with hard x-ray photons Dose distribution and optimization of lens design 8 Agata Tobola- Galus 2018 Radiat. Prot. Dosimetry None To investigate depth dose distribution of a 60MeV proton beam using mesh-formed collimators Simulate proton transport and depth dose distri- bution for verification and optimization of collima- tor design 9 Eric Brost 2018 J. Biomed. Opt. 400 to 800 Characterization of the Cerenkov scatter function for improved dosimetry Simulation of Cerenkov scatter function for dose measurement and validate the experimental data 10 Consuelo Guardi- ola 2017 Med. Phys. None Optimization of mechanical collimation for proton minibeam radiation therapy Evaluation of the influence of collimator material, thickness, distance, and shape on dose distribu- tions and collimator optimization 11 Hassan Abbas 2014 Med. Phys. 0.0443 to 0.1033* (12 to 28 keV) To assess the potential of low-energy focused x- ray beams for radiation therapy and evaluate dose profiles Simulation of dose distribution and beam focusing 12 Rongxiao Zhang 2013 Med. Phys. 400 to 900 To demonstrate the feasibility of using Cerenkov emission for real-time superficial dosimetry imag- ing during radiotherapy Simulating radiation transport, dose deposition, and Cerenkov radiation emission 13 Rongxiao Zhang 2013 Phys. Med. Biol. UV to NIR Examining Cerenkov emission as a method for superficial dosimetry imaging Simulate radiation transport and dose calculation for comparison with experimental data 14 P. K. N. Yu 2013 Phys. Med. Biol. 0.00827 to 0.0248* (50 to 150 kVp) To measure the effects of nasal and facial shields on delivered radiation dose for superficial x-ray treatments Verifying experimental results and assessing the effects of lead shielding Monte Carlo Simulation in Cancer Research Cancer induces a multitude of changes in human tissues, affecting their biochemical, vascular, and morphological properties. Understanding these changes is crucial for developing effective diagnostic and therapeutic strategies. The interaction of light with tissue provides a non- invasive means to detect and quantify these alterations, as different tissue components absorb and scatter light in characteristic ways. Monte Carlo simulations are invaluable tools in this regard, offering detailed and accurate models of light-tissue interactions that can predict how light will behave in cancerous versus healthy tissues. This section explores how MC simulations enhance our understanding of cancer by examining constituent-based optical variations, vascular changes, and morphological changes. By simulating these interactions, researchers can better identify cancerous changes, improve imaging techniques, and refine treatment approaches, ultimately contributing to more effective cancer management. Constituent-Based Optical Variations Cancer induces significant changes in the opti- cal properties of tissues by altering the concentration and distribution of various constituents. These constituents include chromophores such as melanin, hemoglobin, and water, as well as other molecular and structural components like proteins and lipids. MC simulations are highly effective in modeling the complex interactions between light and these altered tissue constituents, enabling researchers to predict the optical differences between cancerous and healthy tissues. This approach aids in identifying specific optical signatures of cancer, which are critical for non-invasive diagnostic techniques. In the following discussion, we will review studies that have leveraged MC simulations to explore these optical variations, highlighting their contributions to improving cancer diagnostics and treatment planning. Fixler and Ankri [219] developed a method for quantitative and noninvasive detection of gold nanorods near the skin surface using diffusion reflection measurements. Monte Carlo simulations were used to model the reflected light intensity in tissue-like phantoms, aiding in understanding the relationship between nanorod concentration and diffusion reflection profiles. Through their analysis, Nan et al. [220] demonstrated the impact of blood content on basal cell carcinoma (BCC) skin tissue optics, showcasing the role of blood as a critical constituent affecting reflectance spectra. Their methodology integrates Monte Carlo simulations and experimental data, emphasizing the significance of tissue constituents in optical modeling of skin cancer. Campoy et al. [221] analyzed the optical behavior of different skin types, including skin with basalioma (skin cancer), using Monte Carlo techniques. The simulations provided insights into optimal wavelengths for photodepilation and skin cancer detection. Figueroa et al. [222] utilized Monte Carlo simulations to optimize the detection of gold nanoparticles in tumor tissue using X-ray fluorescence. The simulations helped determine the optimal experimental setup, including incident spectral energy and backscatter geometry, to improve the signal-to-noise ratio and detection sensitivity. Ney et al. [21, 223] leveraged Monte Carlo simulations to significantly enhance the sensitivity of skin cancer detection through the use of InN plasmonic nanoparticles and terahertz (THz) imaging. Their research focused on detecting minute changes in skin tissue water content and optimizing imaging parameters for early-stage skin cancer diagnosis. Kanakaraj et al. [224] advanced noninvasive optical biopsy techniques for NMSC by employing multilayer Monte Carlo simulations and bimodal spectroscopy to detect key biomarkers in tissue. Their approach, validated through both experimental data and simulations, achieved high accuracy in distinguishing between normal and cancerous tissues, demonstrating potential for clinical diagnostics. Huang et al. [225] investigated the role of human hair, particularly colorless vellus hair, in transmitting ultraviolet radiation to melanocyte stem cells and its implications for melanoma development. Monte Carlo simulations were used to model the optical properties of hair and examine their influence on UV transmission into the skin. Stier et al. [226] developed a machine learning model trained on data obtained from MC simulations to capture sub-diffuse optical property variations. This model is used to render heatmaps from spatial frequency domain imaging (SFDI) data, enabling real-time identification of cancerous and normal skin tissues by highlighting differences in their inherent optical properties. Colas et al. [227] focused on deciphering the impact of metabolic and morphological changes on the optical properties of skin using spatially resolved DR and AF spectroscopy, supported by Modified CudaMCML simulations. Their findings contribute to distinguishing between healthy and pathological skin areas by examining endogenous fluorophore behavior.They also investigated photon contributions to diffuse reflectance spectra for skin cancer diagnosis, employing Monte Carlo simulations to capture variable skin properties in their model [228]. They also established a corrective factor to standardize the absolute magnitude of simulated and clinical spatially-resolved diffuse reflectance spectra. Monte Carlo simulations were employed to compute the correction factor, aiding in the comparison between experimental and simulated spectra [229]. In another study, Ramkumar et al. [20] employed Polarized Monte Carlo (PMC) simulations to model two-layered skin tissue structures, distinguishing between normal, melanoma, and non-melanoma (SCC) conditions by analyzing variations in optical depolarization and diattenuation due to constituent alterations. Their simulations highlighted that melanoma progression, characterized by increased melanin absorption, notably reduced overall depolarization and diattenuation, whereas SCC demonstrated reduced scattering efficiency, similarly decreasing these optical signatures. This study supports the development of non-invasive optical diagnostic tools for early cancer detection. Al-Halawani et al. [230, 231] used Monte Carlo simulations to study how melanin concen- tration affects light-tissue interactions in skin, with implications for photoplethysmography (PPG), a non-invasive optical technique for detecting blood volume changes. In one study, they modeled transmittance-mode pulse oximetry in a finger phantom across Fitzpatrick skin types I, IV, and VI (representing light to dark tones by melanin content), showing that stan- dard SpO 2 calibration curves underestimate oxygen saturation in darker skin due to higher absorption at 660 nm [230]. In the other, they extended analysis to both transmittance and reflectance PPG modes [231], demonstrating that melanin alters photon propagation and signal ratios, especially for red light. These results highlight the importance of accounting for constituent-induced optical variation in non-invasive sensor design. Table 4 outlines the essential information extracted from this subsection. Table 4: Monte Carlo simulation studies on constituent-based optical variations in cancer research(* Spectral range originally given in keV has been converted to nm using standard conversion formulas. The original units are provided in parentheses.) 1 Raghda Al- Halawani 2024 J. Biomed. Opt. 660, 940 Modeling SpO 2 measurement bias due to melanin concentration across Fitzpatrick skin types using a simulated finger phantom Evaluating calibration curve variation with skin pigmentation in pulse oximetry 2 Raghda Al- Halawani 2024 Sci. Rep. 660, 940 Investigating the impact of melanin on PPG signal behavior in reflectance and transmittance modes across different skin types Analysis of photon propagation and signal compo- nent in melanin-rich skin tissue 3 Janaki Ramkumar 2023 Proc. of SPIE 660 Differentiating normal, melanoma, and non- melanoma (SCC) skin based on computed optical depolarization and diattenuation images, aiding diagnostic instrument design. Simulating polarized photon propagation in two- layered skin models to analyze variations in optical properties. 4 Victor Colas 2022 Proc. SPIE 500 to 750 Defining a correction factor to standardize the absolute magnitude of simulated and clinical spatially-resolved diffuse reflectance spectra Computing correction factors and validating them against experimental data 5 Andrew C. Stier 2021 J. Biomed. Opt. 450, 530, 620 Rendering sub-diffuse optical property heatmaps from sd-SFDI images in real time To generate training and validation datasets for the machine learning model 6 Victor Colas 2021 Photonics 365 to 765 To study the depth distribution of SR-DR- detected photons in skin and propose a decom- position of the DR signals related to skin layer contributions Simulating photon transport in skin layers and analyzing depth distribution 7 Victor Colas 2020 Proc. SPIE 365 to 765 Studying the path and propagation depth distri- bution of diffuse reflectance and autofluorescence photons in skin for optical biopsy Simulating photon transport and providing nu- merical evidence of the behavior of detected pho- tons in the tissue 8 Xiyong Huang 2019 Ann. Biomed. Eng. 200 to 900 Investigating the role of human hair in UV transmission and its implications for melanoma development Simulating photon transport in skin and hair models to assess UV transmission 9 Bala Nivetha Kanakaraj 2018 J. Med. Imaging 370 to 650 Generating the spectral signatures of identified biomarkers for NMSC and validate the experimen- tal results Generating spectral signatures and validating optical models 10 Michael Ney 2015 J. Biomed. Opt. None Improving the detection sensitivity of skin cancer using a combination of THz imaging, polarized light imaging, and LSPR Simulating polarized light propagation and scat- tering in skin tissue embedded with nanoparticles and to assess the products of THz polarimetric biomedical imaging 11 R.G. Figueroa 2015 Radiat. Phys. Chem. 0.01476 to 0.01494* (83 to 84 keV) Optimizing detection of gold nanoparticles in tu- mors using XRF and improve diagnostic sensitiv- ity Simulating the fluorescent response of elements and optimizing detection configurations 12 J. Campoy 2015 Rev. Int. Metod. Numer. Calc. Dis. Ing. 400 to 1200 Analyzing optical behavior of skin using Monte Carlo simulation, distinguish between healthy and cancerous skin, optimize photodepilation parameters To enable analysis of optical properties and differentiation between healthy and cancerous skin 13 Miaoqing Nan 2013 Sci. World J. 480 to 700 Studying the effect of blood content on diffuse reflectance spectra of BCC skin tissue Reconstructing diffuse reflectance spectra and studying blood content effects 14 Dror Fixler 2013 J. Biomed. Opt. 450 to 900 Developing a non-invasive method for detecting gold nanorods in subcutaneous tissue for cancer diagnostics To substantiate and extend experimental results under the assumptions presented in Ankri et al. [232] Vascular Changes The vascular system within tissues, characterized by parameters such as blood volume, oxygenation, and vessel density, significantly influences their optical properties. Cancer induces alterations in these parameters, such as increased blood volume from angiogenesis, changes in oxygenation levels, and variations in vessel density and structure. These modifications affect how light is absorbed and scattered within the tissue, which can be distinguished through optical techniques. MC simulations are essential for modeling these complex interactions, enabling researchers to differentiate between healthy and cancerous tissues based on vascular changes. By simulating how light interacts with these modified vascular structures, MC simulations provide insights into the optical signatures of cancer. In the following discussion, we will review studies that have utilized MC simulations to explore these vascular changes, highlighting their contributions to improving cancer diagnostics and treatment planning. Sabotinov et al. [233] developed a copper-bromide laser system for treating vascular malformations, using Monte Carlo simulations to optimize energy deposition at different wavelengths. They found optimal energy deposition at 578.2 nm, particularly effective for targeting deeper blood vessels, improving treatment precision for dermatological conditions. A study by Fredriksson et al. [234] employed Monte Carlo simulations with in vivo laser Doppler flowmetry to model optical microvascular skin properties at 780 nm. This study demonstrated how variations in blood volume and flow velocity impact Doppler spectra, enhancing diagnostic precision in assessing vascular conditions. Nan et al. [220] revealed that blood content variations significantly influence the diffuse reflectance spectra of BCC skin tissue, pointing to vascular changes as a key factor in skin cancer optics. Their work, combining Monte Carlo simulations with in vivo testing, highlights how blood volume affects optical detection in BCC. Using 3D Monte Carlo simulations, Jacques et al. [235] modeled light transport and photoacoustic signal generation in heterogeneous tissues. They demonstrated that this method could effectively predict acoustic signals from blood vessels in the dermis, aiding in the identification of vascular characteristics for improved cancer diagnostics. Stro¨mberg et al. and Jonasson et al. [236,237] both developed fiber-optic systems integrating diffuse reflectance spectroscopy and laser Doppler flowmetry, using Monte Carlo simulations to model light transport in skin. Stro¨mberg et al.’s system focused on measuring blood flow and oxygenation in a three-layered skin model, while Jonasson et al.’s approach assessed hemoglobin oxygen saturation and perfusion during local heating. Both studies provided comprehensive insights into microvascular parameters, enhancing clinical evaluation of vascular responses. Milanic et al. [238] used hyperspectral imaging combined with Monte Carlo simulations to detect xanthelasma as an indicator of hypercholesterolemia. Their model demonstrated effective identification of lipid lesions, with the MNF algorithm showing strong correlation with actual cholesterol levels, offering a potential non-invasive screening tool for cardiovascular risk. Lopez et al. [158] explored the effects of PDT on tumor vasculature through Monte Carlo simulations, specifically assessing how blood vessel damage influences oxygen distribution and therapy effectiveness. Their work introduces the TRSO metric as a means to account for these vascular changes in treatment planning, highlighting the critical role of oxygenation in PDT success. Mowla et al. [239] suggested enhancing skin cancer imaging by integrating reflectance confocal microscopy with laser Doppler flowmetry. They employed Monte Carlo simulations to investigate skin tissue models, factoring in irregular blood cell velocities and concentrations. Doronin et al. [240] utilized a Monte Carlo-based framework with deep-learning neural networks to analyze hyperspectral data, identifying how variations in melanin and blood vessel density affect optical signatures of skin lesions. This method enhances the non-invasive diagnosis of conditions like moles and vitiligo by accurately detecting vascular changes. Tan et al. [241] introduced ”Diffuse in vivo Flow Cytometry” (DiFC) to detect rare fluorescently-labeled circulating cells in the bloodstream. Monte Carlo simulations were employed for optimization of the system and computing detection sensitivity functions, advancing the study of vascular changes in cancer metastasis. Jia et al. in a series of studies utilized a tetrahedron-based Monte Carlo simulation model to improve laser treatments for vascular lesions like port wine stains. At first they developed a model predicting vessel rupture during therapy, focusing on vessel curvature’s impact [242]. Their following studies [130, 243] refined this approach, highlighting the importance of vessel geometry on photon deposition and laser energy delivery. This work enhances the accuracy of predicting treatment outcomes, aiding in the optimization of laser therapies for vascular lesions. A concise summary of the relevant studies is shown in Table 5. Table 5: Overview of research papers investigating vascular changes using Monte Carlo simulations 1 Hao Jia 2021 Appl. Sci. 585 Enhancing the precision of simulating light propagation in skin tissues through Monte Carlo techniques Key method for modeling light transport in skin tissue 2 Hao Jia 2021 Math. Biosci. Eng. 585 Improving modeling of light propagation in complex skin layers Primary method for modeling light propagation in complex skin 3 Hao Jia 2019 Appl. Sci. 595 Establishing a curvature-corrected pressure dam- age (CCPD) model for laser therapy of port wine stains Simulating light propagation and heat deposition 4 Alexander Doronin 2018 Proc. SPIE 400 to 1000 Determining human skin optical properties using Monte Carlo simulations For supervised training and analysis of optical properties 5 Xuefei Tan 2019 Sci. Rep. NIR, 640 nm excitation, 660 nm longpass fil- ter Developing a new tool for detecting extremely rare fluorescently-labeled circulating cells directly in the bloodstream Determining detection sensitivity functions, Ana- lyzing cell speed and depth 6 Tomas Stro¨mberg 2014 J. Biomed. Opt. 475 to 850 (DRS), 780 (LDF) Developing and evaluate a method for measuring oxygen saturation in tissue Modeling light transport in tissue to assess oxygen saturation 7 Steven L. Jacques 2014 Photoacoustics 532 Providing an example for students in photoacous- tic imaging Simulating light transport and energy deposition 8 Hanna Jonasson 2015 Microvasc. Res. 506 to 614 Exploring microcirculatory parameters using in- terventional techniques Modeling light transport and assessing perfusion parameters 9 Matija Milanic 2015 Proc. SPIE 400 to 1090 (Simulation), 400-720 (Ex- periment) Detection of hypercholesterolemia using hyper- spectral imaging Simulating light transport and image creation 10 Alireza Mowla 2016 Sensors 850 Improving skin cancer imaging by combining reflectance confocal microscopy and laser Doppler flowmetry Simulating photon-tissue interactions, validate imaging technique 11 N. Lopez 2016 J. Photochem. Photobiol. B: Biol. 630 Improvment of dosimetry protocols and treatment planning in PDT; Proposing a model for simulat- ing spatial and temporal distribution of oxygen and photosensitizer in PDT Computing light fluence in tissue 12 Miaoqing Nan 2013 Sci. World J. 480 to 700 To study the effect of blood content on diffuse reflectance spectra of BCC skin tissue Reconstruct diffuse reflectance spectra and study blood content effects 13 Ingemar Fredriks- son 2008 J. Biomed. Opt. 780 Developing a skin model for velocity-resolved Doppler measurements Simulating Doppler spectra and evaluation of perfusion models 14 Ognian Sabotinov 2005 Proc. SPIE 532, 578.2, 585 Evaluation of the effectiveness of CuBr lasers in medical applications Simulating photon migration and energy deposi- tion in tissue Morphological Changes Cancer’s insidious progression is often marked by profound morphological changes at the cellular and tissue levels. These alterations include variations in cell size, shape, and organization, as well as disruptions in the extracellular matrix. Such morphological transformations impact the way light interacts with tissue, altering scattering and absorption patterns. MC simulations provide a powerful tool to model these changes, allowing researchers to visualize and quantify the differences between healthy and cancerous tissues. By simulating the interaction of light with morphologically altered tissues, MC simulations help in identifying distinct optical signatures that can be used for early cancer detection and precise treatment planning. The subsequent analysis will explore various studies that have employed MC simulations to investigate these morphological changes, showcasing their pivotal role in advancing cancer diagnostics and therapy. Du et al. [244] utilized Mueller matrix imaging and Monte Carlo simulations to investigate characteristic features of cancerous tissues. The simulations examined the relationship between tissue microstructures and Mueller matrix features, providing insights for distinguishing healthy and pathological tissues based on polarization parameters. Heijblom et al. [245] used Monte Carlo simulations to investigate the appearance of breast cancer in Rembrandt’s painting ”Bathsheba at Her Bath.” The simulations modeled light interaction with skin and underlying carcinoma spheres, revealing that a bluish appearance is only visible for carcinomas at specific depths. Fanjul-Velez et al. [246] created a tool using Monte Carlo simulations to tailor laser surgery parameters for non-melanoma skin cancer, enhancing treatment precision. Using Monte Carlo simulations, Gareau et al. [247] modeled diffuse light remittance from pigmented skin lesions, integrating histopathological data to assess spectral components crucial for diagnosis. This approach clarified how lesion morphology and chromophore concentration impact light scattering and absorption, enhancing non- invasive diagnostic techniques for skin lesions. The study by Tchvialeva et al. [248] investigated the use of polarized laser speckles to enhance skin cancer diagnostics, focusing on melanoma. Using Monte Carlo simulations, they demonstrated that image speckle patterns differentiate skin lesions better than free-space speckles, improving non-invasive diagnosis of structural abnormalities. Mowla et al. [239] proposed an innovative approach combining RCM and LDF, utilizing Monte Carlo simulations to enhance skin cancer detection by concurrently assessing morphological and vascular biomarkers. Their method shows promising improvement in sensitivity and specificity for skin cancer imaging, validated through experiments with dynamic turbid media. Lopez et al. [158] used Monte Carlo simulations to model the dosimetry of PDT, introducing the tumor reactive singlet oxygen (TRSO) metric to optimize treatment planning. Their simulations, which include factors like blood vessel damage and photosensitizer distribution, aim to enhance PDT efficacy by understanding its morphological impact on tumors. Campbell et al. [157] employed 3D Monte Carlo radiation transfer modeling to study light penetration in tumor tissues during PDT. The simulations compared a clustered tumor model with a smooth model, revealing that the clustered model allows for deeper light penetration. Iralieva et al. [249] developed a digital phantom algorithm using Monte Carlo simulations and fractal noise to model dermatoscopic images of skin cancer. The algorithm simulates optical properties based on ABCD criteria (asymmetry, borders, colors, diameter), enhancing non-invasive diagnostics and the study of skin cancer morphology, especially for rare tumors. Tabassum et al. [250] created a two-layer inverse model to improve preclinical tumor imaging with spatial frequency domain imaging, validated through Monte Carlo simulations that calculated detection sensitivity functions. Huang et al. [251] investigated the role of human hair, particularly colorless vellus hair, in transmitting ultraviolet radiation to melanocyte stem cells and its implications for melanoma development. Colas et al. [227] utilized Modified CudaMCML-based simulations to explore how carcinogenesis-related morphological changes in skin affect DR and AF photon distribution. This analysis aids in developing optical multimodal biopsy techniques for cutaneous carcinoma diagnosis. In another study [228], they analyzed depth distribution of photons in skin via spatially-resolved diffuse reflectance spectroscopy, using Monte Carlo simulations. Their decomposition model highlights the contributions of individual skin layer. In a 2022 study [252] they developed a novel inverse problem-solving scheme to accurately estimate layer-wise optical properties within a five- layer skin model. Using Monte Carlo simulations, their method enhances the diagnostic precision for skin pathologies by emphasizing the unique optical characteristics of each skin layer. Table 6 presents a brief overview of the main insights covered here. Table 6: Summary of studies on morphological changes in cancer using Monte Carlo simulations 1 Victor Colas 2022 Proc. SPIE 365 to 765 Enhancing the estimation of optical properties in a five-layer skin model by utilizing spatially resolved diffuse reflectance spectra through a layer-by-layer method Used for numerical simulations to generate spectra for the optimization process 2 Xiyong Huang 2020 J. Opt. Soc. Am. A 290 to 400 Investigating the impact of hair removal on UV transmission and melanoma risk Simulating UV transmission and absorption in skin models 3 Syeda Tabassum 2018 J. Biomed. Opt. 659, 691, 731, and 851 Developing a two-layer LUT inversion algorithm for improved accuracy in subcutaneous tumor imaging in preclinical models Generating LUTs for the two-layer inversion algorithm 4 Malica B. Iralieva 2018 Proc. Saratov Fall Meet. None Creating a digital phantom to simulate der- matoscopy images of skin cancer Simulating normal skin background in digital phantoms 5 F´elix Fanjul-V´elez 2015 Laser Phys. 633 Developing a predictive tool for optimal laser surgery in non-melanoma skin cancer Calculating optical propagation in biological tissues 6 Daniel Gareau 2014 Proc. SPIE 350 to 950 Modeling and understand diffuse light remittance from pigmented lesions in skin Simulating light transport and remittance from skin 7 Lioudmila Tchvial- eva 2014 Proc. SPIE 663 Improving skin cancer diagnostics using polarized light Simulating the detected scattered co- and cross- polarized light 8 Michelle Heijblom 2014 J. Biophotonics 450, 550, 633 Investigating if breast cancer masses at different depths can be perceived by the human eye and the colors they manifest Simulating light-tissue interactions and investigate optical properties of breast cancer 9 E. Du 2014 J. Biomed. Opt. 650 Differentiating characteristic features of cancerous tissues using polarization imaging Examining the relationship between polarization parameters and tissue microstructures Discussion Monte Carlo simulations have revolutionized the understanding and application of light- tissue interactions in dermatology and oncology. Despite the significant advancements, sev- eral challenges and limitations remain, necessitating further research and development to enhance the accuracy and clinical applicability of these simulations. Limitations of Current MC Simulation Models Current MC simulation models, while powerful, face several limitations. One primary chal- lenge is the accurate modeling of complex tissue structures and their optical properties. Variations in tissue composition, heterogeneity, and dynamic changes due to physiological conditions can lead to discrepancies between simulated and actual results. For example, the influence of patient-specific anatomical variations and tissue irregularities often complicates the simulation accuracy. Additionally, the high computational cost and time required for running detailed MC simulations pose a barrier to their routine clinical use. Standardiza- tion and validation of MC models across different platforms and clinical settings are also crucial hurdles that need to be addressed to ensure consistency and reliability in simulation outcomes. Regulatory issues further complicate the integration of MC simulation findings into routine dermatological practice, as these models must undergo rigorous validation and approval processes. Table 7 provides a comprehensive overview of the challenges and issues identified in light-skin interaction research studies. This compilation highlights the various aspects of study that require attention to enhance the accuracy and applicability of MC simulation models. Emerging Trends and Future Research Areas Despite these challenges, several emerging trends hold promise for overcoming current limitations and expanding the utility of MC simulations. The integration of MC simulations with artificial intelligence (AI) and machine learning (ML) algorithms is one such trend, offering transformative potential to address issues of computational efficiency and modeling complexity. AI and ML can enhance the predictive power of MC simulations by identifying patterns and optimizing parameters that would be computationally expensive to explore otherwise. Additionally, the combination of MC simulations with advanced imaging technologies, such as hyperspectral imaging and optical coherence tomography, can provide more comprehensive datasets, improving the accuracy of tissue characterization and diagnosis. Another promising area is the development of hybrid models that combine MC simulations with other computational methods, such as finite element modeling, to capture a broader range of physical phenomena. These advancements underline the growing role of computational innovations in expanding the applicability and reliability of MC simulations, creating opportunities to bridge theoretical insights with practical applications. In the following, we explore specific avenues where AI and ML methods can be effectively leveraged to address these limitations and unlock new possibilities. Integration of AI and ML in Monte Carlo Simulations Artificial intelligence (AI) and machine learning (ML) offer powerful tools for enhancing computational methods by optimizing processes, handling complex datasets, and reducing computational burdens. These technologies have the potential to improve key aspects of Monte Carlo simulations, including computational efficiency, modeling accuracy for tissue heterogeneity, and robust validation frameworks. Let’s take a closer look at some key instances and areas where AI and ML can be leveraged to optimize MC simulations. Computational Efficiency and Acceleration MC simulations are computationally intensive, particularly when modeling complex tissue geometries or high-resolution datasets. AI and ML significantly mitigate these demands through: • Surrogate Modeling : ML algorithms, such as neural networks, are trained on precomputed simulation datasets to predict outcomes with high fidelity. These models emulate MC simulations at a fraction of the computational cost, enabling rapid iterations [253–255]. • Dynamic Load Balancing : AI systems dynamically allocate resources across GPU threads, optimizing computational loads and preventing bottlenecks during runtime [256–258]. • Adaptive Sampling : ML-driven variance reduction techniques improve the convergence rate of simulations by selectively prioritizing high-impact regions in the computational domain [259–261]. These strategies enable real-time or near-real-time MC simulations for applications such as intraoperative imaging and dynamic treatment planning. Enhanced Accuracy and Precision AI and ML refine the accuracy of MC simulations by addressing variability in input parameters and tissue heterogeneity: • Parameter Optimization : AI-driven optimization algorithms estimate optical parameters, such as absorption and scattering coefficients, using clinical or experimental data, ensuring realistic inputs for simulations [262–264]. • Heterogeneous Tissue Representation : ML models trained on large datasets accurately capture tissue heterogeneities, such as chromophore distributions, vascular architecture, and cellular structures, improving simulation realism [262, 265–267]. • Systematic Error Correction : Post-processing ML algorithms identify and correct biases or discrepancies between simulation outputs and experimental results [268]. These enhancements elevate the reliability and clinical applicability of MC simulations across diverse research and treatment settings. Validation, Calibration, and Uncertainty Quantification Ensuring the reliability of MC simulations is critical for clinical adoption. AI and ML contribute to this domain by: • Data-Driven Validation : ML models compare simulation outputs with large experimental datasets to identify discrepancies and validate results [269–271]. • Probabilistic Modeling : AI-based uncertainty quantification methods, such as Bayesian networks, assess the confidence levels of simulation predictions [272–275]. • Adaptive Calibration : ML algorithms iteratively adjust simulation parameters to align outputs with empirical observations, ensuring robustness across diverse scenarios [276, 277]. These tools enhance the credibility of MC simulations, making them more robust for translational applications. Scalability for Large-Scale Clinical Implementation AI and ML facilitate the transition of MC simulations from research environments to routine clinical practice by addressing scalability: • Automated Workflows : ML algorithms automate processes such as segmentation, parameter estimation, and data visualization, reducing manual effort [278–281]. • Resource Optimization : AI-powered adaptive frameworks adjust computational complexity based on application needs, ensuring efficient resource utilization [282–284]. • Cloud-Based Solutions : Cloud computing platforms enhanced with AI capabilities democratize access to computationally intensive MC simulations for smaller clinics and research institutions [285, 286]. These innovations enable widespread adoption of MC simulations across healthcare settings. Conclusion and Future Directions AI and ML have transformed the landscape of Monte Carlo simulations, addressing traditional limitations while unlocking new possibilities. From accelerating computational efficiency to enhancing accuracy and enabling real-time integration, these advancements position MC simulations as pivotal tools in modern biomedical research and clinical practice. Future research should focus on refining these integrations, with a particular emphasis on empirical validation and cross-disciplinary applications. As AI technologies continue to evolve, their convergence with MC simulations promises to revolutionize diagnostic and therapeutic strategies across dermatology, oncology, and beyond. Present Open Questions Several open questions remain that could stimulate further research in this field. For instance, how can MC simulations be optimized to reduce computational costs while maintaining accu- racy? What are the most effective ways to integrate MC simulations with real-time imaging and AI-based analysis for dynamic and patient-specific applications? Additionally, exploring the potential of MC simulations in new therapeutic areas, such as personalized medicine and targeted drug delivery, could open up innovative treatment approaches. Addressing these questions requires interdisciplinary collaboration and continuous advancements in computa- tional techniques and biomedical technologies. Monte Carlo simulations, in addition to serving as robust tools for validating experimental outcomes, offer exceptional flexibility for systematically exploring and optimizing key pa- rameters in experimental setups and therapeutic device designs. These simulations provide significant potential to enhance the precision and efficacy of therapies by enabling detailed studies on parameters such as source-detector distances, illumination or irradiation angles, spot sizes, detector configurations, and probe designs. Such opportunities present compelling avenues for focused research and innovative design developments, particularly in therapies like PDT, PTT, and radiotherapy, where precise energy delivery, accurate dosimetry, and personalized treatment designs critically influence clinical outcomes. Further dedicated re- search in simulation-driven refinement of these parameters can reduce dependence on em- pirical trial-and-error approaches, foster greater personalization of treatments, and facilitate the advancement of more effective therapeutic equipment and methodologies. By tackling these challenges and exploring emerging trends, researchers can further enhance the applicability and impact of MC simulations in dermatology and oncology, ultimately leading to improved patient outcomes and more effective treatment strategies. Table 7: Compilation of Challenges and Issues Identified in Light-Skin Interaction Research Studies Optical Penetration and Interaction Depth Limitation Shallow light penetration restricts deeper tissue analysis and treatment. Wavelength Dependency Different wavelengths affect penetration and scattering, complicating measurements. Scattering and Absorption Difficulties in quantifying scattering and absorption in heterogeneous tissues. Superficial Tissue Restriction Techniques often limited to superficial tissues, affecting deeper insights. External Light Interference Ambient light affects measurement accuracy, especially in clinical settings. Model Assumptions and Simplifications Homogeneous Assumptions Assumptions of uniformity don’t capture tissue heterogeneity. Geometric Simplifications Simplified geometries may not reflect complex anatomical structures. Static Conditions Ignoring dynamic changes like blood flow can lead to inaccuracies. Boundary Conditions Simplified treatment of boundaries can affect model accuracy. Oxygen and Metabolic Assumptions Misrepresenting tissue responses by neglecting oxygen and metabolic factors. Lack of Experimental Validation Theoretical models without validation may not be reliable. Measurement and Experimental Challenges Instrumentation Sensitivity and Accuracy Limited by the capabilities of measurement instruments. Invasive Techniques Ethical and practical limits on the use of invasive methods. Calibration and Standardization Inconsistent protocols can lead to variable data. Environmental and Human Error External factors and human errors introduce variability. Real-time Monitoring Constraints Difficulty in acquiring and processing data in real-time. Reproducibility Challenges in achieving consistent results across studies. Computational Constraints Algorithm Efficiency Inefficient algorithms limit scalability and resolution. Resource Intensity High computational needs can be prohibitive. Data Management Challenges in handling and analyzing large datasets. Model Resolution Trade-offs between detail and computational feasibility. Parameter Sensitivity Sensitivity to variations complicates interpretation. Uncertainty Quantification Difficulties in managing uncertainties in models. Data Availability and Parameter Uncertainty Optical Property Data Limited availability of accurate data affects model precision. Inter-patient Variability Variability in tissue properties complicates generalization. Measurement Techniques Accuracy of optical property measurements is limited. Integration of Diverse Data Differences in methodologies hinder data integration. Parameter Estimation Estimation challenges lead to uncertainties in results. Confidence in Results Uncertainties and variability reduce confidence in findings. Clinical and Practical Challenges Translation to Practice Challenges in applying research findings clinically. Safety and Efficacy Ensuring the safety and effectiveness of new methods. Customization and Personalization Tailoring treatments to individual patients is complex. Regulatory Compliance Navigating regulatory and ethical requirements. Training and Expertise Need for specialized training limits widespread adoption. Economic and Resource Constraints Cost and resource limitations affect scalability. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments Financial support was provided by the Australian Research Council’s Discovery Projects’ funding scheme (DP210103342). References 1. Alhallak K, Omran D, Tomi S and Abdulhafid A 2021 J Clin Derm Ther 7 081 Google Scholar 2. Pashazadeh A, Boese A and Friebe M 2019 Journal of Dermatological Treatment Google Scholar 3. Lima-Bessa K M and Menck C F 2005 Current Biology 15 R58–R61 Google Scholar 4. Paul C N 1918 The Influence of Sunlight in the Production of Cancer of the Skin (HK Lewis & Company, Limited) Google Scholar 5. Hall A F 1950 Archives of Dermatology and Syphilology 61 589–610 Google Scholar 6. Granstein R D and Sober A J 1982 Proceedings of the Society for Experimental Biology and Medicine Google Scholar 7. Coelho M M V, Matos T R and Apetato M 2016 Clinics in dermatology 34 563–570 Google Scholar 8. Zaidi M R, De Fabo E C, Noonan F P and Merlino G 2012 Cancer research 72 1591–1595 Google Scholar 9. Emri G, Paragh G, T´osaki A´, Janka E, Koll´ar S, Hegedu˝s C, Gell´en E, Horkay I, Koncz G and Remenyik E´ 2018 Journal of Photochemistry and Photobiology B: Biology 185 169–175 Google Scholar 10. Baranoski G V and Krishnaswamy A 2005 Simulation of light interaction with human skin. Google Scholar 11. Baranoski G V and Krishnaswamy A 2004 Revista de Inform´atica Te´orica e Aplicada 11 33–62 Google Scholar 12. Lu J Q, Hu X H and Dong K 2000 Applied Optics 39 5890–5897 Google Scholar 13. Tuchin V V, Yaroslavsky A N, Jacques S L and Anderson R 2010 Biophotonics for dermatology: science & applications Google Scholar 14. Tuchin V V 1993 Static and dynamic light scattering in medicine and biology 1884 234–272 Google Scholar 15. Yang M F, Tuchin V V and Yaroslavsky A N 2009 Light-Based Therapies for Skin of Color 1–44 Google Scholar 16. Chen T F, Baranoski G V, Kimmel B W and Miranda E 2015 ACM Transactions on Graphics (TOG) Google Scholar 17. Karsten A E and Smit J E 2012 Photochemistry and photobiology 88 469–474 Google Scholar 18. Palamoni O P, de Magalh˜aes A C, Moriyama L T, de Sousa M V P and Fortunato T C 2022 Monte carlo simulations to study light propagation through the skin of different phototypes Tissue Optics and Photonics II vol 12147 (SPIE) pp 28–34 Google Scholar 19. Valentine R, Wood K, Brown C, Ibbotson S and Moseley H 2012 Physics in Medicine & Biology 57 Google Scholar 20. Ramkumar J and Unni S N 2023 Polarized monte carlo simulation for the study of skin cancer Women in Optics and Photonics in India 2022 vol 12638 (SPIE) pp 128–130 Google Scholar 21. Ney M and Abdulhalim I 2016 Comprehensive monte-carlo simulator for optimization of imaging parameters for high sensitivity detection of skin cancer at the thz Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications XIII vol 9721 (SPIE) pp 147–166 Google Scholar 22. Al-Halawani R, Chatterjee S and Kyriacou P A 2022 Monte carlo simulation of the effect of human skin melanin in light-tissue interactions 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (IEEE) pp 1598–1601 Google Scholar 23. Lee C H K, Lee J K and Lim H S 1998 Monte carlo simulation to measure light dosimetry within the biological tissue Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol. 20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No. 98CH36286) vol 6 (IEEE) pp 2967–2969 Google Scholar 24. Herrmann B H and Hornberger C 2018 Current Directions in Biomedical Engineering 4 275–278 Google Scholar 25. Krasnikov I, Seteikin A and Roth B 2019 Biomedical Engineering Letters 9 327–337 Google Scholar 26. Periyasamy V and Pramanik M 2017 IEEE reviews in biomedical engineering 10 122–135 Google Scholar 27. Muraro S, Battistoni G and Kraan A 2020 Frontiers in Physics 8 567800 Google Scholar 28. Ai X, Mu J and Xing B 2016 Theranostics 6 2439 Google Scholar 29. Goyal R, Husain S, Wilson K, Chopra H, Pahwa R, Loganathan M and Sharma R 2023 Exploration of Medicine 4 782–812 Google Scholar 30. Farhana A 2023 International Journal of Molecular Sciences 24 3493 Google Scholar 31. Orthaber K, Pristovnik M, Skok K, Peri´c B, Maver U et al. 2017 Journal of nanomaterials 2017 Google Scholar 32. Kanugo A 2022 Current Pharmaceutical Biotechnology Google Scholar 33. J Sanchez-Barcelo E and D Mediavilla M 2014 Recent patents on endocrine, metabolic & immune drug discovery 8 1–8 Google Scholar 34. Ma C M C, Chetty I J, Deng J, Faddegon B, Jiang S B, Li J, Seuntjens J, Siebers J V and Traneus E 2020 Medical physics 47 e1–e18 Google Scholar 35. Benov L 2015 Medical principles and practice 24 14–28 Google Scholar 36. Omer H 2021 Saudi journal of biological sciences 28 5585–5592 Google Scholar 37. Allen C, Borak T B, Tsujii H and Nickoloff J A 2011 Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 711 150–157 Google Scholar 38. Kim B M, Hong Y, Lee S, Liu P, Lim J H, Lee Y H, Lee T H, Chang K T and Hong Y 2015 International journal of molecular sciences 16 26880–26913 Google Scholar 39. Fang Q and Boas D A 2009 Optics express 17 20178–20190 Google Scholar 40. Yu L, Nina-Paravecino F, Kaeli D and Fang Q 2018 Journal of biomedical optics 23 010504–010504 Google Scholar 41. Yan S and Fang Q 2020 Biomedical Optics Express 11 6262–6270 Google Scholar 42. Yuan Y, Yan S and Fang Q 2020 Biomedical Optics Express 12 147–161 Google Scholar 43. Fang Q 2010 Biomedical optics express 1 165–175 Google Scholar 44. Yao R, Intes X and Fang Q 2015 Biomedical optics express 7 171–184 Google Scholar 45. Alerstam E, Svensson T and Andersson-Engels S 2008 Journal of biomedical optics 13 060504–060504 Google Scholar 46. Alerstam E, Lo W C Y, Han T D, Rose J, Andersson-Engels S and Lilge L 2010 Biomedical optics express 1 658–675 Google Scholar 47. Doronin A and Meglinski I 2011 Biomedical optics express 2 2461–2469 Google Scholar 48. Goorley T, James M, Booth T, Brown F, Bull J, Cox L, Durkee J, Elson J, Fensin M, Forster R et al. 2014 Features of mcnp6 SNA+ MC 2013-joint international conference on supercomputing in nuclear applications+ Monte Carlo (EDP Sciences) p 06011 Google Scholar 49. Agostinelli S, Allison J, Amako K a, Apostolakis J, Araujo H, Arce P, Asai M, Axen D, Banerjee S, Barrand G et al. 2003 Nuclear instruments and methods in physics research section A: Accelerators, Google Scholar 50. Sarrut D, Bardi‘es M, Boussion N, Freud N, Jan S, L´etang J M, Loudos G, Maigne L, Marcatili S, Google Scholar 51. Kawrakow I 2001 NRCC Report Pirs-701 Google Scholar 52. Bank D 2015 Google Scholar 53. Battistoni G, Bauer J, Boehlen T T, Cerutti F, Chin M P, Dos Santos Augusto R, Ferrari A, Ortega P G, Kozl-owska W, Magro G et al. 2016 Frontiers in oncology 6 116 Google Scholar 54. Augusto R, Bauer J, Bouhali O, Cuccagna C, Gianoli C, Kozl-owska W, Ortega P, Tessonnier T, Google Scholar 55. Zhu C and Liu Q 2013 Journal of biomedical optics 18 050902–050902 Google Scholar 56. Wang S, Chen J, Zhang F, Zhao M, Cui X and Chen S 2022 Medical Physics 49 1209–1215 Google Scholar 57. Arridge S, Schweiger M, Hiraoka M and Delpy D 1993 Medical physics 20 299–309 Google Scholar 58. Mehrabi M, Setayeshi S, Ardehali S H and Arabalibeik H 2017 Journal of Biomedical Optics 22 Google Scholar 59. Asllanaj F, Contassot-Vivier S, Liemert A and Kienle A 2014 Journal of Biomedical Optics 19 015002– 015002 Google Scholar 60. Long F, Li F, Intes X and Kotha S P 2016 Journal of biomedical optics 21 036003–036003 Google Scholar 61. Wang A, Lu R and Xie L 2015 Applied Optics 55 95–103 Google Scholar 62. Tarvainen T, Vauhkonen M, Kolehmainen V and Kaipio J 2006 International journal for numerical methods in engineering 65 383–405 Google Scholar 63. Srinivasan S, Ghadyani H R, Pogue B W and Paulsen K D 2010 Biomedical optics express 1 398–413 Google Scholar 64. Wang Z, Ha S and Kim K 2012 Evaluation of finite-element-based simulation model of photoacoustics in biological tissues Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy vol 8320 (SPIE) pp 470–478 Google Scholar 65. van Rossum M, Mestrom R and van Beurden M 2019 Effect of finite precision on em simulations for high-contrast biological media at low frequencies 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA) (IEEE) pp 0523–0527 Google Scholar 66. Xia M, Li D, Wang L and Wang D 2019 Journal of Innovative Optical Health Sciences 12 1930007 Google Scholar 67. Freutel M, Schmidt H, Du¨rselen L, Ignatius A and Galbusera F 2014 Clinical Biomechanics 29 363–372 Google Scholar 68. Cheng Y, Shen Y, Gao Y, Wen Y, Lv Z, Wen N, Wang E, Li G, Bo Y and Peng Q 2024 Frontiers in Physics 12 1407471 Google Scholar 69. Hoshi Y and Yamada Y 2016 Journal of biomedical optics 21 091312–091312 Google Scholar 70. Nagliˇc P, Vidoviˇc L, Milaniˇc M, Randeberg L L and Majaron B 2019 Osa Continuum 2 905–922 Google Scholar 71. Hennessy R, Markey M K and Tunnell J W 2015 Journal of biomedical optics 20 027001–027001 Google Scholar 72. Suchoski B, Stage S, Gurung H and Baccam P 2022 Frontiers in Applied Mathematics and Statistics Google Scholar 73. Fang Q and Yan S 2019 Journal of Biomedical Optics 24 115002–115002 Google Scholar 74. Young A R 1997 Physics in Medicine & Biology 42 789 Google Scholar 75. Schwarz M, Aguirre J, Soliman D, Buehler A and Ntziachristos V 2016 Unmixing chromophores in human skin with a 3d multispectral optoacoustic mesoscopy system Photons Plus Ultrasound: Google Scholar 76. Fodor L and Sobec R 2020 Aesthetic applications of intense pulsed light 13–23 Google Scholar 77. Tseng S H, Bargo P, Durkin A and Kollias N 2009 Optics express 17 14599–14617 Google Scholar 78. Barun V and Ivanov A 2010 Journal of Applied Spectroscopy 77 73–79 Google Scholar 79. Mourant J R, Canpolat M, Brocker C, Esponda-Ramos O, Johnson T M, Quintana D, Stetter K and Freyer J P 1999 Basic mechanisms of light scattering in tissue 1999 International Conference on Biomedical Optics vol 3863 (SPIE) pp 22–26 Google Scholar 80. Mourant J R, Canpolat M, Brocker C, Esponda-Ramos O, Johnson T M, Matanock A, Stetter K and Freyer J P 2000 Journal of biomedical optics 5 131–137 Google Scholar 81. Jacques S L 2011 Journal of Innovative Optical Health Sciences 4 1–7 Google Scholar 82. Tuchin V V et al. 2015 Tissue optics Light Scattering Methods and Instruments for Medical Diagnostics, Third Edition (Society of Photo-Optical Instrumentation Engineers (SPIE) Bellingham, WA, USA) Google Scholar 83. Popp A K, Valentine M T, Kaplan P D and Weitz D A 2003 Applied optics 42 2871–2880 Google Scholar 84. Andersen P E, Dam J S, Petersen P M and Bjerring P 1997 Local diffuse reflectance from three-layered skin tissue structures Optical Tomography and Spectroscopy of Tissue: Theory, Instrumentation, Google Scholar 85. Cui W and Ostrander L E 1992 IEEE Transactions on Biomedical Engineering 39 194–201 Google Scholar 86. Savenkov S, Oberemok E, Mamilov S, Esman S and Asimov M 2011 Journal of Applied Spectroscopy Google Scholar 87. Svaasand L O, Spott T, Fishkin J B, Pham T, Tromberg B J and Berns M W 1999 Physics in Medicine & Biology 44 801 Google Scholar 88. Van Gemert M, Jacques S L, Sterenborg H and Star W 1989 IEEE Transactions on biomedical engineering 36 1146–1154 Google Scholar 89. Lister T, Wright P A and Chappell P H 2012 Journal of biomedical optics 17 090901–090901 Google Scholar 90. Nickell S, Hermann M, Essenpreis M, Farrell T J, Kr¨amer U and Patterson M S 2000 Physics in Medicine & Biology 45 2873 Google Scholar 91. Mourant J R, Boyer J, Hielscher A H and Bigio I J 1996 Optics letters 21 546–548 Google Scholar 92. Sharma S and Banerjee S 2003 Journal of Optics A: Pure and Applied Optics 5 294 Google Scholar 93. Shimojo Y, Nishimura T, Hazama H, Ozawa T and Awazu K 2020 Journal of biomedical optics 25 Google Scholar 94. Schmitt J, Zhou G, Walker E and Wall R 1990 JOSA A 7 2141–2153 Google Scholar 95. Calabro K, Curtis A, Galarneau J R, Krucker T and Bigio I J 2011 Journal of biomedical optics 16 Google Scholar 96. Kohen E, Santus R and Hirschberg J G 1995 Photobiology (Elsevier) Google Scholar 97. Lu J Q, Dong K and Hu X H 2000 Effects of rough interfaces on a converging laser beam propagating in a skin tissue phantom Optical Biopsy III vol 3917 (SPIE) pp 176–183 Google Scholar 98. Ogura Y, Shimano M, Bise R, Yamashita T, Katagiri C and Sato I 2023 Experimental Dermatology Google Scholar 99. Baranoski G 2010 Bio-optical properties of human skin light and skin interactions ed gvg baranoski and a krishnaswamy Google Scholar 100. Anders A, Altheide H and Tronnier H 1997 Optics of human skin: Uv effects investigated with dye lasers Skin Cancer and UV Radiation (Springer) pp 205–210 Google Scholar 101. El-Gammal S, Hoffmann K, Steiert P, Gassmu¨ller J and Altmeyer P 1993 Noninvasive Methods for the Quantification of Skin Functions: An Update on Methodology and Clinical Applications 133–144 Google Scholar 102. Altshuler G B and Tuchin V V 2009 Physics behind light-based systems: skin and hair follicle interactions with light Cosmetics Applications of Laser & Light-Based Systems (Elsevier) pp 49– 123 Google Scholar 103. Finlayson L, Barnard I R, McMillan L, Ibbotson S H, Brown C T A, Eadie E and Wood K 2022 Google Scholar 104. Ash C, Dubec M, Donne K and Bashford T 2017 Lasers in medical science 32 1909–1918 Google Scholar 105. Khan M H, Chess S, Choi B, Kelly K M and Nelson J S 2004 Lasers in Surgery and Medicine 35 93–95 Google Scholar 106. Kwon K, Son T, Lee K J and Jung B 2009 Lasers in medical science 24 605–615 Google Scholar 107. Zhang H, Zhao H, Zhao X, Xu C, Franklin D, V´azquez-Guardado A, Bai W, Zhao J, Li K, Monti G Google Scholar 108. Chung H J, Cheng J, Gonzalez M and Al-Janahi S 2020 Dermatologic Surgery 46 e146–e153 Google Scholar 109. Esnouf A, Wright P A, Moore J C and Ahmed S 2007 Acupuncture & electro-therapeutics Research 32 Google Scholar 110. Saghafi S, Withford M, Farhadi M, Ghaderi R, Granmayeh A, Ghoranneviss Z and Moravej F 2006 Effects of laser beam shapes on depths of penetration in dermatology Biophotonics and New Therapy Frontiers vol 6191 (SPIE) pp 53–64 Google Scholar 111. Altshuler G, Anderson R, Manstein D, Zenzie H and Smirnov M 2001 Lasers in Surgery and Medicine: Google Scholar 112. Nelson J S 1991 Plastic and reconstructive surgery 88 723–731 Google Scholar 113. Anderson R R, Farinelli W, Laubach H, Manstein D, Yaroslavsky A N, Gubeli III J, Jordan K, Neil G R, Shinn M, Chandler W et al. 2006 Lasers in Surgery and Medicine: The Official Journal of the American Society for Laser Medicine and Surgery 38 913–919 Google Scholar 114. Parrish J and Deutsch T 1984 IEEE journal of quantum electronics 20 1386–1396 Google Scholar 115. Tong L, Zhao Y, Huff T B, Hansen M N, Wei A and Cheng J X 2007 Advanced Materials 19 3136–3141 Google Scholar 116. Genina E, Bashkatov A N, Tuchin V V, Altshuler G B and Yaroslavskii I 2008 Quantum Electronics Google Scholar 117. Prado T P, Zanchetta F C, Barbieri B, Aparecido C, Melo Lima M H and Araujo E P 2023 Life 13 Google Scholar 118. Sommer A P, Pinheiro A L, Mester A R, Franke R P and Whelan H T 2001 Journal of clinical laser medicine & surgery 19 29–33 Google Scholar 119. Gupta A, Dai T and Hamblin M R 2014 Lasers in medical science 29 257–265 Google Scholar 120. Teuschl A, Balmayor E R, Redl H, Van Griensven M and Dungel P 2015 Dermatologic Surgery 41 Google Scholar 121. Jacques S L 2010 Monte carlo modeling of light transport in tissue (steady state and time of flight) Google Scholar 122. Ren N, Liang J, Qu X, Li J, Lu B and Tian J 2010 Optics express 18 6811–6823 Google Scholar 123. Margallo-Balb´as E and French P J 2007 Optics express 15 14086–14098 Google Scholar 124. Wang L, Jacques S L and Zheng L 1995 Computer methods and programs in biomedicine 47 131–146 Google Scholar 125. Meglinski I V 2003 Method monte carlo in optical diagnostics of skin and skin tissues Third International Conference on Photonics and Imaging in Biology and Medicine vol 5254 (SPIE) pp 30–43 Google Scholar 126. Tsui S Y, Wang C Y, Huang T H and Sung K B 2018 Biomedical optics express 9 1531–1544 Google Scholar 127. Urso P, Lualdi M, Colombo A, Carrara M, Tomatis S and Marchesini R 2007 Physics in Medicine & Biology 52 N229 Google Scholar 128. Miller I D and Veitch A R 1993 Lasers in surgery and medicine 13 565–571 Google Scholar 129. Wan S, Anderson R R and Parrish J A 1981 Photochemistry and Photobiology 34 493–499 Google Scholar 130. Jia H, Chen B and Li D 2021 Applied Sciences 11 2998 Google Scholar 131. Donner C, Weyrich T, d’Eon E, Ramamoorthi R and Rusinkiewicz S 2008 ACM transactions on graphics (TOG) 27 1–12 Google Scholar 132. Papadimitroulas P 2017 Physica Medica 41 136–140 Google Scholar 133. Badal A, Zbijewski W, Bolch W and Sechopoulos I 2014 Medical Physics 41 564–565 Google Scholar 134. Ljungberg M 2011 Monte carlo simulations for therapy imaging Journal of Physics: Conference Series Google Scholar 135. Paganetti H 2014 The British journal of radiology 87 20140293 Google Scholar 136. Tessonnier T, Mairani A, Cappucci F, Mirandola A, Freixas G V, Molinelli S, Donetti M and Ciocca M 2014 Applied Radiation and Isotopes 83 155–158 Google Scholar 137. Wilson B C and Patterson M S 2008 Physics in Medicine & Biology 53 R61 Google Scholar 138. Castano A P, Demidova T N and Hamblin M R 2004 Photodiagnosis and photodynamic therapy 1 Google Scholar 139. Darlenski R and Fluhr J W 2013 Journal of biomedical optics 18 061208–061208 Google Scholar 140. Morton C, Szeimies R M, Sidoroff A and Braathen L 2013 Journal of the European Academy of Dermatology and Venereology 27 536–544 Google Scholar 141. Morton C, Szeimies R M, Sidoroff A and Braathen L 2013 Journal of the European Academy of Dermatology and Venereology 27 672–679 Google Scholar 142. Wong R L and Lai T Y 2013 Journal of ophthalmic & vision research 8 359 Google Scholar 143. Shishkova N, Kuznetsova O and Berezov T 2012 Cancer Biology & Medicine 9 9 Google Scholar 144. Star W M 1990 Lasers in Medical Science 5 107–113 Google Scholar 145. Wilson B C, Patterson M S and Lilge L 1997 Lasers in medical science 12 182–199 Google Scholar 146. Tyrrell J, Thorn C, Shore A, Campbell S and Curnow A 2011 British Journal of Dermatology 165 Google Scholar 147. Ochsner M 1997 Journal of Photochemistry and Photobiology B: Biology 39 1–18 Google Scholar 148. Dolmans D E, Fukumura D and Jain R K 2003 Nature reviews cancer 3 380–387 Google Scholar 149. Henderson B W, Busch T M and Snyder J W 2006 Lasers in Surgery and Medicine: The Official Journal of the American Society for Laser Medicine and Surgery 38 489–493 Google Scholar 150. RP M S 2018 Asian Pacific Journal of Cancer Prevention: APJCP 19 279 Google Scholar 151. Quintanar L, Fabila D, Stolik S and de la Rosa J 2013 An irradiation system for photodynamic therapy with a fiber-optic sensor for measuring tissue oxygen 8th Iberoamerican Optics Meeting and Google Scholar 152. van Leeuwen-van Zaane F, Gamm U A, van Driel P B, Snoeks T J, de Bruijn H S, van der Ploeg- van den Heuvel A, Sterenborg H J, L¨owik C W, Amelink A and Robinson D J 2014 Journal of Biomedical Optics 19 015010–015010 Google Scholar 153. Campbell C L, Christison C, Brown C T A, Wood K, Valentine R M and Moseley H 2015 3d monte carlo radiation transfer modelling of photodynamic therapy Biophotonics South America vol 9531 (SPIE) pp 150–162 Google Scholar 154. Campbell C L, Wood K, Valentine R, Brown C T A and Moseley H 2015 Physics in Medicine & Biology Google Scholar 155. Fanjul-V´elez F, Salas-Garc´ıa I, Torre-Celeizabal C, Zverev M and Arce-Diego J L 2015 Analysis of low intensity laser therapy as adjuvant to photodynamic therapy in nonmelanoma skin cancer 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE) pp 6900–6903 Google Scholar 156. Campbell C L, Brown C T A, Wood K and Moseley H 2016 Physics in Medicine & Biology 61 7507 Google Scholar 157. Campbell C L, Wood K, Brown C T A and Moseley H 2016 New insights into photodynamic therapy treatment through the use of 3d monte carlo radiation transfer modelling Photonic Therapeutics and Diagnostics XII vol 9689 (SPIE) pp 65–74 Google Scholar 158. Lopez N, Mulet R and Rodr´ıguez R 2016 Journal of Photochemistry and Photobiology B: biology 160 Google Scholar 159. Zhao K, Chen W, Li T, Yan P, Qin Z and Zhao H 2018 Fast determination of the spatially distributed photon fluence for light dose evaluation of pdt Photonics in Dermatology and Plastic Surgery 2018 vol 10467 (SPIE) pp 66–75 Google Scholar 160. LaRochelle E P, Marra K, LeBlanc R E, Chapman M S, Maytin E V and Pogue B W 2019 Google Scholar 161. Walter A B, Simpson J, Jenkins J L, Skaar E P and Jansen E D 2020 Photodiagnosis and photodynamic therapy 29 101624 Google Scholar 162. Doronin A, Yakovlev V V and Bagnato V S 2024 Biomedical Optics Express 15 1682–1693 Google Scholar 163. Vasilieva E, Vladyko I, Yakovlev V and Doronin A 2025 Machine learning-based optimization of photodynamic therapy for human skin affected by malignant melanoma: a monte carlo study and 3d renderings Dynamics and Fluctuations in Biomedical Photonics XXII vol 13318 (SPIE) pp 13–18 Google Scholar 164. Li X, Lovell J F, Yoon J and Chen X 2020 Nature reviews Clinical oncology 17 657–674 Google Scholar 165. Guan Q, Zhou L L, Zhou L N, Li M, Qin G X, Li W Y, Li Y A and Dong Y B 2020 Chemical Communications 56 7793–7796 Google Scholar 166. Miao Z H, Wang H, Yang H, Li Z, Zhen L and Xu C Y 2016 ACS Applied Materials & Interfaces 8 Google Scholar 167. Xu Y, Shan Y, Cong H, Shen Y and Yu B 2018 Current pharmaceutical design 24 4060–4076 Google Scholar 168. Hwang S, Nam J, Jung S, Song J, Doh H and Kim S 2014 Nanomedicine 9 2003–2022 Google Scholar 169. Taylor M L, Wilson Jr R E, Amrhein K D and Huang X 2022 Bioengineering 9 200 Google Scholar 170. Jung H S, Verwilst P, Sharma A, Shin J, Sessler J L and Kim J S 2018 Chemical Society Reviews 47 Google Scholar 171. Sun Z, Xie H, Tang S, Yu X F, Guo Z, Shao J, Zhang H, Huang H, Wang H and Chu P K 2015 Google Scholar 172. Zou Q, Abbas M, Zhao L, Li S, Shen G and Yan X 2017 Journal of the American Chemical Society Google Scholar 173. Wu X, Suo Y, Shi H, Liu R, Wu F, Wang T, Ma L, Liu H and Cheng Z 2020 Nano-Micro Letters 12 Google Scholar 174. Zhang Z, Suo H, Zhao X, Sun D, Fan L and Guo C 2018 ACS applied materials & interfaces 10 Google Scholar 175. Mammari N and Duval R E 2023 Microorganisms 11 2084 Google Scholar 176. Chen Y, Gao Y, Chen Y, Liu L, Mo A and Peng Q 2020 Journal of Controlled Release 328 251–262 Google Scholar 177. Cui Q, Yuan H, Bao X, Ma G, Wu M and Xing C 2020 ACS Applied Bio Materials 3 4436–4443 Google Scholar 178. Chen X, Yang L, Wu Y, Wang L and Li H 2023 ACS omega 8 46362–46375 Google Scholar 179. Sun R, Chen H, Sutrisno L, Kawazoe N and Chen G 2021 Science and technology of advanced materials Google Scholar 180. Joshi R, Jadhao M and Ghosh S K 2023 Recent trends in the applications of nanocomposites in cancer theranostics Green Sustainable Process for Chemical and Environmental Engineering and Science (Elsevier) pp 283–320 Google Scholar 181. Wang P, Chen B, Zhan Y, Wang L, Luo J, Xu J, Zhan L, Li Z, Liu Y and Wei J 2022 Pharmaceutics Google Scholar 182. Didychuk C L, Ephrat P, Chamson-Reig A, Jacques S L and Carson J J 2009 Nanotechnology 20 Google Scholar 183. Grosges T, Barchiesi D, Kessentini S, Gr´ehan G and de la Chapelle M L 2011 Biomedical Optics Express 2 1584–1596 Google Scholar 184. Manuchehrabadi N, Chen Y, LeBrun A, Ma R and Zhu L 2013 Journal of biomechanical engineering Google Scholar 185. Daoudi K, Van Es P, Manohar S and Steenbergen W 2013 Journal of biomedical optics 18 116009– 116009 Google Scholar 186. Manuchehrabadi N and Zhu L 2014 International Journal of Hyperthermia 30 349–361 Google Scholar 187. Gong F, Hongyan Z, Papavassiliou D V, Bui K, Lim C and Duong H M 2014 Nanotechnology 25 Google Scholar 188. Ren Y, Qi H, Chen Q and Ruan L 2017 International journal of heat and mass transfer 106 212–221 Google Scholar 189. Zhang X, Qiu S, Wu S, Li Z and Li H 2018 Simulation of temperature distribution in tumor photothermal treatment Biophotonics and Immune Responses XIII vol 10495 (SPIE) pp 47–57 Google Scholar 190. Xu Y, Long S, Yang Y, Zhou F, Dong N, Yan K, Wang B, Zeng Y, Du N, Li X et al. 2019 Theoretical Biology and Medical Modelling 16 1–11 Google Scholar 191. Wang Y, Leng S, Huang J, Shu M and Papavassiliou D V 2020 International Journal for Numerical Methods in Biomedical Engineering 36 e3275 Google Scholar 192. Hsieh Z H, Fan C H, Ho Y J, Li M L and Yeh C K 2020 Scientific reports 10 17406 Google Scholar 193. Kim D and Kim H 2021 International Journal of Molecular Sciences 22 11091 Google Scholar 194. Kim D, Kang S and Kim H 2021 Applied Sciences 11 1103 Google Scholar 195. Sun C, Ji C, Li Y, Kuang J and Wu J 2021 International Journal of Thermal Sciences 164 106924 Google Scholar 196. Kim D and Kim H 2023 Pharmaceutics 15 911 Google Scholar 197. Wachsmuth L, Chongsathidkiet P, Liu Y, Odion R, Srinivasan E, Mendoza A, Edwards R, Canning A, Willoughby G, Hinton J et al. 2023 Neuro-Oncology Advances 5 iii31 Google Scholar 198. Kim H J, Um S H, Kang Y G, Shin M, Jeon H, Kim B M, Lee D and Yoon K 2023 Journal of Computational Design and Engineering 10 947–958 Google Scholar 199. Reddy I V K, Elmaadawy S, Furlani E P and Jornet J M 2023 Scientific Reports 13 14643 Google Scholar 200. Zerakni F, Dib A A and Attili A 2024 Lasers in Medical Science 39 130 Google Scholar 201. Abbas H, Mahato D N, Satti J and MacDonald C 2014 Medical Physics 41 041702 Google Scholar 202. Thomas S J and Hoole A C 2004 Physics in Medicine & Biology 49 4919 Google Scholar 203. Webb S 1993 The physics of three dimensional radiation therapy: Conformal radiotherapy, radiosurgery and treatment planning (CRC Press) Google Scholar 204. Chow J C and Jubran S 2023 Micromachines 14 1230 Google Scholar 205. Guardiola C, Peucelle C and Prezado Y 2017 Medical physics 44 1470–1478 Google Scholar 206. Stewart J, Sahgal A, Lee Y, Soliman H, Tseng C L, Detsky J, Husain Z, Ho L, Das S, Maralani P J Google Scholar 207. Peter K and Butson M J 2013 Physics in Medicine & Biology 58 N95 Google Scholar 208. Zhang R, Fox C J, Glaser A K, Gladstone D J and Pogue B W 2013 Physics in Medicine & Biology Google Scholar 209. Zhang R, Glaser A K, Gladstone D J, Fox C J and Pogue B W 2013 Medical physics 40 101914 Google Scholar 210. Brost E and Watanabe Y 2018 Journal of biomedical optics 23 105007–105007 Google Scholar 211. Tobola-Galus A, Swakon´ J and Olko P 2018 Radiation Protection Dosimetry 180 351–354 Google Scholar 212. Girou D, Ford E, Wade C, Van Aarle C, Uliyanov A, Hanlon L, Tomsick J A, Zoglauer A, Collon M J, Google Scholar 213. Wang M H, Kim A, Ruschin M, Tan H, Soliman H, Myrehaug S, Detsky J, Husain Z, Atenafu E G, Google Scholar 214. Rafiei M M, Parsaei S, Kaur P, Singh K, Bu¨yu¨kyıldız M and Kurudirek M 2022 Biomedical Physics & Engineering Express 8 025002 Google Scholar 215. Hao S, Xiong Y, Guo S, Gao J, Chen X, Zhang R, Liu L and Wang J 2023 Biomedical Optics Express Google Scholar 216. Wang Y, Du J, Lin H, Guan X, Zhang L, Li J and Gu L 2024 Medical Physics Google Scholar 217. Barghash R, Martin T W, Prebble A R and Leary D 2025 Journal of Applied Clinical Medical Physics Google Scholar 218. Lee U S, Kim S w, Shin J B, Jeong C, Goh Y, Park M J, Kwak J, Song S Y and Cho B 2025 Scientific Reports 15 5499 Google Scholar 219. Fixler D and Ankri R 2013 Journal of biomedical optics 18 061226–061226 Google Scholar 220. Nan M, He Q et al. 2013 The Scientific World Journal 2013 Google Scholar 221. Campoy J, Alcarria R and Gonzalez-Marcos A 2015 REVISTA INTERNACIONAL DE METODOS NUMERICOS PARA CALCULO Y DISENO EN INGENIERIA 31 161–170 Google Scholar 222. Figueroa R, Santiban˜ez M, Malano F and Valente M 2015 Radiation Physics and Chemistry 117 Google Scholar 223. Ney M and Abdulhalim I 2015 Journal of Biomedical Optics 20 125007–125007 Google Scholar 224. Kanakaraj B N and Narayanan Unni S 2018 Journal of Medical Imaging 5 014506–014506 Google Scholar 225. Huang X, Protheroe M D, Al-Jumaily A M, Paul S P, Chalmers A N, Wang S, Diwu J and Liu W 2019 Annals of biomedical engineering 47 2372–2383 Google Scholar 226. Stier A C, Goth W, Hurley A, Brown T, Feng X, Zhang Y, Lopes F C, Sebastian K R, Ren P, Fox M C et al. 2021 Journal of Biomedical Optics 26 096007–096007 Google Scholar 227. Colas V, Daul C, Khairallah G, Amouroux M and Blondel W 2020 Spatially resolved diffuse reflectance and autofluorescence photon depth distribution in human skin spectroscopy: a modeling study Optics in Health Care and Biomedical Optics X vol 11553 (SPIE) pp 82–96 Google Scholar 228. Colas V, Blondel W, Khairallah G, Daul C and Amouroux M 2021 Proposal for a skin layer-wise decomposition model of spatially-resolved diffuse reflectance spectra based on maximum depth photon distributions: a numerical study Photonics vol 8 (MDPI) p 444 Google Scholar 229. Colas V, Daul C, Khairallah G, Amouroux M, Perrin-Mozet C and Blondel W 2022 Theoretical definition and experimental validation of an correction factor to standardize the absolute magnitude of simulated and clinical spatially-resolved diffuse reflectance spectra Biomedical Applications of Light Scattering XII vol 11974 (SPIE) pp 7–14 Google Scholar 230. Al-Halawani R, Qassem M and Kyriacou P A 2024 Journal of Biomedical Optics 29 S33305 Google Scholar 231. Al-Halawani R, Qassem M and Kyriacou P A 2024 Scientific Reports 14 8145 Google Scholar 232. Ankri R, Peretz V, Motiei M, Popovtzer R and Fixler D 2012 International journal of nanomedicine Google Scholar 233. Sabotinov O and Stoykova E 2005 Copper-bromide laser system for treatment of dermatological malformations 13th International School on Quantum Electronics: Laser Physics and Applications vol 5830 (SPIE) pp 449–453 Google Scholar 234. Fredriksson I, Larsson M and Str¨omberg T 2008 Journal of biomedical optics 13 014015–014015 Google Scholar 235. Jacques S L 2014 Photoacoustics 2 137–142 Google Scholar 236. Str¨omberg T, Karlsson H, Fredriksson I, Nystr¨om F H and Larsson M 2014 Journal of Biomedical Optics 19 057002–057002 Google Scholar 237. Jonasson H, Fredriksson I, Pettersson A, Larsson M and Str¨omberg T 2015 Microvascular research Google Scholar 238. Milanic M, Bjorgan A, Larsson M, Str¨omberg T and Randeberg L L 2015 Detection of hypercholesterolemia using hyperspectral imaging of human skin European Conference on Biomedical Optics (Optica Publishing Group) p 95370C Google Scholar 239. Mowla A, Taimre T, Lim Y L, Bertling K, Wilson S J, Prow T W, Soyer H P and Raki´c A D 2016 Google Scholar 240. Doronin A, Meglinski I, Bykov A V and Rushmeier H 2018 Determination of human skin optical properties from hyper spectral data with deep-learning neural networks (conference presentation) Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis vol 10489 (SPIE) p 104890U Google Scholar 241. Tan X, Patil R, Bartosik P, Runnels J M, Lin C P and Niedre M 2019 Scientific reports 9 3366 Google Scholar 242. Jia H, Chen B and Li D 2019 Applied Sciences 9 5478 Google Scholar 243. Jia H, Chen B, Li D and Jin Y 2021 Mathematical Biosciences and Engineering 18 2455–2472 Google Scholar 244. Du E, He H, Zeng N, Sun M, Guo Y, Wu J, Liu S and Ma H 2014 Journal of biomedical optics 19 Google Scholar 245. Heijblom M, Meijer L M, van Leeuwen T G, Steenbergen W and Manohar S 2014 Journal of biophotonics 7 323–331 Google Scholar 246. Fanjul-V´elez F, Salas-Garc´ıa I and Arce-Diego J L 2014 Laser Physics 25 025606 Google Scholar 247. Gareau D, Jacques S and Krueger J 2014 Monte carlo modeling of pigmented lesions Photonic Therapeutics and Diagnostics X vol 8926 (SPIE) pp 122–127 Google Scholar 248. Tchvialeva L, Lee T K, Markhvida I, Zeng H, Doronin A and Meglinski I 2014 Enhanced diagnostic of skin conditions by polarized laser speckles: phantom studies and computer modeling Photonic Therapeutics and Diagnostics X vol 8926 (SPIE) pp 128–137 Google Scholar 249. Iralieva M B, Myakinin O O, Bratchenko I A and Zakharov V P 2018 Modeling of skin cancer dermatoscopy images Saratov Fall Meeting 2017: Optical Technologies in Biophysics and Medicine XIX vol 10716 (SPIE) pp 67–74 Google Scholar 250. Tabassum S, Pera V, Greening G, Muldoon T J and Roblyer D 2018 Journal of biomedical optics 23 Google Scholar 251. Huang X, Protheroe M D, Al-Jumaily A M, Paul S P and Chalmers A N 2020 JOSA A 37 807–812 Google Scholar 252. Colas V, Daul C, Khairallah G, Amouroux M and Blondel W 2022 Improved estimation of the optical properties in a skin five-layer model from spatially resolved diffuse reflectance spectra using a layer-by-layer approach and optimized combinations of wavelengths and source-detector distances Biomedical Applications of Light Scattering XII vol 11974 (SPIE) pp 40–45 Google Scholar 253. Gorissen D, Couckuyt I, Demeester P, Dhaene T and Crombecq K 2010 Journal of machine learning research.-Cambridge, Mass. 11 2051–2055 Google Scholar 254. Sajjadinia S S, Carpentieri B, Shriram D and Holzapfel G A 2022 Computers in Biology and Medicine Google Scholar 255. Sacks M S, Motiwale S, Goodbrake C and Zhang W 2022 Journal of biomechanical engineering 144 Google Scholar 256. Badal A and Badano A 2009 Medical physics 36 4878–4880 Google Scholar 257. Zhu A, Chang Q, Xu J and Ge W 2023 Powder Technology 428 118782 Google Scholar 258. Hailat E, Russo V, Rushaidat K, Mick J, Schwiebert L and Potoff J 2014 International Journal of Parallel, Emergent and Distributed Systems 29 379–400 Google Scholar 259. Bhatia H, Carpenter T S, Ing´olfsson H I, Dharuman G, Karande P, Liu S, Oppelstrup T, Neale C, Google Scholar 260. Zheng Q and Zwicker M 2019 Learning to importance sample in primary sample space Computer Graphics Forum vol 38 (Wiley Online Library) pp 169–179 Google Scholar 261. Gabri´e M, Rotskoff G M and Vanden-Eijnden E 2022 Proceedings of the National Academy of Sciences Google Scholar 262. Hokr B H and Bixler J N 2021 Scientific Reports 11 6561 Google Scholar 263. Kholodtsova M N, Daul C, Loschenov V B and Blondel W C 2016 Optics Express 24 12682–12700 Google Scholar 264. G¨okkan M O and Engin M 2017 Journal of Innovative Optical Health Sciences 10 1650027 Google Scholar 265. Ebrahimi S and Bagchi P 2022 Journal of the Royal Society Interface 19 20220306 Google Scholar 266. Vasan R, Rowan M P, Lee C T, Johnson G R, Rangamani P and Holst M 2020 Frontiers in physics 7 Google Scholar 267. Wertheimer Z A, Bar C and Levin A 2022 Optics Express 30 9854–9868 Google Scholar 268. Raayai Ardakani M, Yu L, Kaeli D R and Fang Q 2022 Journal of Biomedical Optics 27 083019–083019 Google Scholar 269. Yaroslavsky A N, Juliano A F, Adnan A, Selting W J, Iorizzo T W, Carroll J D, Sonis S T, Duncan C N, London W B and Treister N S 2021 Diagnostics 11 2207 Google Scholar 270. Nizam N I, Ochoa M, Smith J T, Gao S and Intes X 2022 Journal of Biomedical Optics 27 083016– 083016 Google Scholar 271. Macdonald C M, Arridge S and Powell S 2020 Journal of Biomedical Optics 25 085002–085002 Google Scholar 272. Cerutti F, Kaplan L M, Kimmig A and S¸ensoy M 2022 Machine Learning 1–43 Google Scholar 273. Feng J, Lansford J L, Katsoulakis M A and Vlachos D G 2020 Science advances 6 eabc3204 Google Scholar 274. Park I and Grandhi R V 2011 AIAA journal 49 1038–1045 Google Scholar 275. Urbina A, Mahadevan S and Paez T L 2012 International Journal for Uncertainty Quantification 2 Google Scholar 276. Su¨rer O¨, Plumlee M and Wild S M 2024 Technometrics 66 157–171 Google Scholar 277. Oh B K, An H, Shin E j, Kim J h and Hong S E 2022 Monthly Notices of the Royal Astronomical Society 515 693–705 Google Scholar 278. Sarrut D, Etxebeste A, Mun˜oz E, Krah N and L´etang J M 2021 Frontiers in Physics 9 738112 Google Scholar 279. Field M, Hardcastle N, Jameson M, Aherne N and Holloway L 2021 Physics and Imaging in Radiation oncology 19 13–24 Google Scholar 280. Cui S, Tseng H H, Pakela J, Ten Haken R K and El Naqa I 2020 Medical physics 47 e127–e147 Google Scholar 281. Peng Z, Fang X, Yan P, Shan H, Liu T, Pei X, Wang G, Liu B, Kalra M K and Xu X G 2020 Medical physics 47 2526–2536 Google Scholar 282. Yang C and Kumar M 2019 Journal of Guidance, Control, and Dynamics 42 1218–1236 Google Scholar 283. Celik N and Son Y J 2012 International journal of production research 50 843–865 Google Scholar 284. Pirayeshshirazinezhad R, Biedron´ S G, Cruz J A D, Gu¨itr´on S S and Mart´ınez-Ram´on M 2022 IEEE Access 10 45643–45662 Google Scholar 285. Miras H, Jim´enez R, Miras C and Gom‘a C 2013 Physics in Medicine & Biology 58 N125 Google Scholar 286. Fang Q and Yan S 2022 Journal of Biomedical Optics 27 083008–083008 Google Scholar Information & Authors Information Version history V1 Version 1 03 August 2025 Peer review timeline Published Photodiagnosis and Photodynamic Therapy Version of Record 1 Dec 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords monte carlo simulation photodynamic therapy photothermal therapy radiotherapy skin cancer Authors Affiliations Mahdi Qaryan 0000-0002-7641-204X [email protected] The University of Queensland View all articles by this author Iman Kafian-Attari Ita-Suomen yliopisto - Kuopion kampus View all articles by this author Isaac O. Afara The University of Queensland View all articles by this author Metrics & Citations Metrics Article Usage 450 views 207 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mahdi Qaryan, Iman Kafian-Attari, Isaac O. Afara. Monte Carlo Simulations of Light-Skin Interactions: Implications for Therapeutic and Oncological Applications. Authorea . 03 August 2025. DOI: https://doi.org/10.22541/au.175422254.45857165/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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